• Sorted by Date • Classified by Publication Type • Classified by Research Category • Sorted by First Author Last Name • Classified by Author Last Name •

Sorted by First Author Last Name

• Anderson • Andrew • Arnav • Aydin • Bai • Bairi • Balakrishnan • Bartels • Bhatt • Bilmes • Bittremieux • Bulyko • Cetin • Chan • Chen • Chung • Consortium • Cotter • Das • Dolhansky • Durham • Elliott • Ellis • Everitt • Filali • Gao • Gillenwater • Gowdy • Guillory • Gunther • Halloran • Harada • Hoffman • House • Hristov • Hu • Iyer • Jegelka • Ji • Kawahara • Kilanski • King • Kirchhoff • Klammer • Kohn • Kothawade • Kumaran • Kumari • Lavania • Lei • Li • Libbrecht • Lin • Liu • Livescu • Lu • Ma • Malkin • Mausam • May • Meila • Mirzasoleiman • Morgan • Narang • Narasimhan • Niepert • P. Singh • Pentney • Pernkopf • Ping-Chen • Powers • Prabhakara • Raj • Reynolds • Richardson • Schmidt • Schreiber • Singh • Stupakov • Subramanya • T. • Takiguchi • Thulasidasan • Tschiatschek • Verma • Vuduc • Wang • Wei • Wyatt • Yang • Yao • Zhang • Zhou • Zweig •

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Anderson

• D. Anderson and J. Bilmes. MOOD: A Concurrent C++-Based Music Language. In International Computer Music Conference, 1992.
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• D. Anderson and J. Bilmes. Concurrent Real-Time Music in C++. In USENIX C++ Conference, 1989.
  Details     BibTeX     Download: [pdf] (185.9kB )  [ps.gz] (70.8kB )  

Andrew

• Galen Andrew and Jeff Bilmes. Backpropagation in Sequential Deep Belief Networks. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Lake Tahoe, Nevada, December 2013. NIPS Workshop on Deep Learning
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• Galen Andrew, Raman Arora, Karen Livescu, and Jeff Bilmes. Deep Canonical Correlation Analysis. In International Conference on Machine Learning (ICML), Atlanta, Georgia, 2013.
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• Galen Andrew and Jeff Bilmes. Memory-efficient inference in dynamic graphical models using multiple cores. In Fifteenth International Conference on Artificial Intelligence and Statistics (AISTAT), La Palma, Canary Islands, April 2012.
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• Galen Andrew and Jeff Bilmes. Sequential Deep Belief Networks. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Kyoto, Japan, 2012.
  Details     BibTeX     Download: [pdf] (1.1MB )  [ps.gz] (2.5MB )  [poster, pdf] (1.3MB )  

Arnav

• Jeff Bilmes Arnav Das, Gantavya Bhatt, Megh Bhalerao, Vianne Gao, Rui Yang. Accelerating Batch Active Learning Using Continual Learning Techniques. Technical Report Arxiv, 2023.
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Aydin

• Zafer Aydin, Ajit Singh, Jeff Bilmes, and William Noble. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinformatics, 12(1):154, 2011.
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• Zafer Aydin, Sheila Reynolds, Jeffrey Bilmes, and William Noble. A Dynamic Bayesian Network Approach for Protein Secondary Structure Prediction. In Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), Tucson, Arizona, April 2009.
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Bai

• Wenruo Bai, Jeffrey Bilmes, and William Stafford Noble. Submodular generalized matching for peptide identification in tandem mass spectrometry. IEEE/ACM transactions on Computational Biology and Bioinformatics, IEEE, 2018.
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• Wenruo Bai, William Noble, and Jeff Bilmes. Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions. In Neural Information Processing Society (NeurIPS, formerly NIPS), Montreal, Canada, December 2018.
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• Wenruo Bai and Jeff Bilmes. Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions. In International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018. http://proceedings.mlr.press/v80/bai18a.html
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• Wenruo Bai and Jeffrey Bilmes. Greed is Still Good: Maximizing Monotone Submodular+Supermodular Functions. Arxiv, abs/1801.07413, Jan 2018.
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• Wenruo Bai, Jeffrey Bilmes, and William S. Noble. Bipartite Matching Generalizations for Peptide Identification in Tandem Mass Spectrometry. In 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), ACM SIGBio, ACM SIGBio, Seattle, WA, October 2016. Winner of the Best Paper Award, ACM-BCB 2016
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• Wenruo Bai, Rishabh Iyer, Kai Wei, and Jeff Bilmes. Algorithms for Optimizing the Ratio of Submodular Functions. In International Conference on Machine Learning (ICML), New York, NY, July 2016.
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Bairi

• Ramakrishna Bairi, Ganesh Ramakrishnan, Rishabh Iyer, and Jeff Bilmes. Multi-Topic Summarization in DAG-Structured Topic Hierarchies via Submodular Mixtures. In Proceedings of the Association for Computational Linguistics/Asian Federation of Natural Language Processing (ACL-IJCNLP), Beijing, China, 2015.
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Balakrishnan

• Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, and Jeff Bilmes. Diverse Client Selection for Federated Learning via Submodular Maximization. In International Conference on Learning Representations (ICLR-2022), 2022.
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• Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, and Jeff Bilmes. Diverse Client Selection for Federated Learning: Submodularity and Convergence Analysis. In ICML 2021 International Workshop on Federated Learning for User Privacy and Data Confidentiality, Virtual, July 2021.
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Bartels

• Chris Bartels and Jeff A. Bilmes. Creating Non-Minimal Triangulations for Use in Inference in Mixed Stochastic / Deterministic Graphical Models. Machine Learning Journal, 84(3):249–289, 2011.
  Details     BibTeX     Download: (unavailable)
• Chris Bartels and Jeff Bilmes. Graphical Models for Integrating Syllabic Information. Computer Speech and Language, 24(4):685–697, 2010.
  http://dx.doi.org/10.1016/j.csl.2009.11.001"> http://dx.doi.org/10.1016/j.csl.2009.11.001
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• Chris Bartels and Jeff Bilmes. Graphical Models for Integrating Syllabic Information. Technical Report UWEETR-2009-0007, University of Washington, Dept. of EE, 2009.
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2009-0007.html
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• Chris Bartels and Jeff Bilmes. Using Syllable Nuclei Locations to Improve Automatic Speech Recognition in the Presence of Burst Noise. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brisbane, Australia, September 2008.
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• Chris Bartels and Jeff Bilmes. Use of Syllable Nuclei Locations to Improve ASR. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Kyoto, Japan, December 2007.
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• Chris Bartels and Jeff Bilmes. Non-Minimal Triangulations for Mixed Stochastic/Deterministic Graphical Models. In Uncertainty in Artificial Intelligence (UAI), AUAI, Cambridge, MA, July 2006.
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• Chris Bartels and Jeff Bilmes. Focused State Transition Information in ASR. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Cancun, Mexico, Nov/Dec 2005.
  Details     BibTeX     Download: [pdf] (303.9kB )  [ps.gz] (315.1kB )  
• Chris Bartels, Kevin Duh, Jeff Bilmes, Katrin Kirchhoff, and Simon King. Genetic Triangulation of Graphical Models for Speech and Language Processing. In European Conf. on Speech Communication and Technology (Eurospeech), Lisbon, Portugal, September 2005.
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• Chris Bartels and Jeff Bilmes. Elimination is not enough: Non-minimal triangulations for graphical models. Technical Report UWEETR-2004-0010, University of Washington, Dept. of Electrical Engineering, 2004. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2004-0010.html
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Bhatt

• Gantavya Bhatt, Arnav Das, and Jeff Bilmes. Deep Submodular Peripteral Networks. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2024. Neurips-2024 spotlight
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• Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff Bilmes, Simon Shaolei Du, Kevin Jamieson, Jordan T. Ash, and Robert D. Nowak. An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, 2024. [Poster] Accepted at ACL '24 (Findings)
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• Gantavya Bhatt and Jeff Bilmes. Tighter m-DPP Coreset Sample Complexity Bounds. In ICML 2021 Workshop: SubSetML: Subset Selection in Machine Learning: From Theory to Practice, Virtual, July 2021.
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Bilmes

• Jeff A Bilmes, Gantavya Bhatt, and Arnav M Das. How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions. Technical Report Arxiv, 2026.
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• Jeff Bilmes. Submarine: SUBModularity for ARtificial INtelligencE and machine learning. Online Software System, 2025. https://submarine.page
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• Jeff Bilmes. Submodular Optimization, MIT Press, 2023. Chapter 6.9 in book: Probabilistic Machine Learning: Advanced Topics by. Kevin Murphy
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• Jeffrey Bilmes. Submodularity In Machine Learning and Artificial Intelligence. Arxiv, abs/2202.00132, Oct 2022. https://arxiv.org/abs/2202.00132
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• Jeff Bilmes. Dynamic Bayesian Networks and the GMTK Documentation. August 2020. https://github.com/melodi-lab/gmtk and https://github.com/melodi-lab/gmtk/blob/master/documentation.pdf
  Details     BibTeX     Download: (unavailable)
• Jeff Bilmes. Deep Submodular Synergies. International Conference on Learning Representations, 2019, Invited Speaker, June 2019. Web link https://slideslive.com/38917384
  Details     BibTeX     Download: (unavailable)
• Jeff Bilmes. Algorithms and Data Structures for Exact Computation of Marginals. In Marloes Maathuis, Mathias Drton, Steffen Lauritzen and Martin Wainwright, editors, Handbook of Graphical Models, pp. 83–110, Chapman & Hall/CRC Handbooks of Modern Statistical Methods, 2018.
  Details     BibTeX     Download: (unavailable)
• Jeffrey Bilmes and Wenruo Bai. Deep Submodular Functions. Arxiv, abs/1701.08939, Jan 2017.
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• Jeff Bilmes. Summarizing Large Data Sets. Harvard University, IACS Seminar, March 2015. https://youtu.be/R5OSSXi2wgg
  Details     BibTeX     Download: [pdf] (25.6MB )  [ps.gz] (22.3MB )  
• Jeff Bilmes. Submodularity in Machine Learning Applications. Twenty-Ninth Conference on Artificial Intelligence, AAAI-15 Tutorial Forum, January 2015.
  Details     BibTeX     Download: [pdf] (42.3MB )  [ps.gz] (162.7MB )  
• Jeff Bilmes. Mathematical Properties of Submodularity with Applications to Machine Learning. Machine Learning Summer School Tutorial, Reykjavik, Iceland, May 2014.
  Details     BibTeX     Download: [pdf] (27.4MB )  [ps.gz] (235.7MB )  
• Jeff Bilmes, Krste Asanovi\'c, CW Chin, and Jim Demmel. Author Retrospective for Optimizing Matrix Multiply using PHiPAC: a Portable High-Performance ANSI C Coding Methodology. In International Conference on Supercomputing (ICS) 25th Anniversary Volume, ACM, 2014. 978-1-4503-2840-1/14/06 Retrospective on a "most influential ICS paper in 25 years" award given to our PHIPAC paper from 1997 below.
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• Jeff Bilmes. Deep Mathematical Properties of Submodularity with Applications to Machine Learning. NIPS Conference 2013 Tutorial, December 2013. https://youtu.be/ZycBUGLD22E?si=xWjd6WhnOqBHm8Lp
  http://nips.cc/Conferences/2013/Program/event.php?ID=3688
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• Jeff Bilmes. Submodularity and Big Data. International Conference on Learning Representations, 2013, Invited Speaker, May 2013. Web link https://sites.google.com/site/representationlearning2013/program-details/invited-speakers and video http://techtalks.tv/talks/tba-invited-talk/58092/
  Details     BibTeX     Download: [pdf] (16.2MB )  [ps.gz] (14.7MB )  
• Jeff Bilmes and Amar Subramanya. Parallel Graph-Based Semi-Supervised Learning. In Ron Bekkerman, Mikhail Bilenko, and John Langford, editors, Scaling Up Machine Learning, pp. 307–330, Cambridge University Press, January 2012.
  Details     BibTeX     Download: [pdf] (941.4kB )  [ps.gz] (2.7MB )  
• Jeff Bilmes. Dynamic Graphical Models. IEEE Signal Processing Magazine, 27(6):29–42, November 2010.
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• Jeff Bilmes and Hui Lin. Online Adaptive Learning for Speech Recognition Decoding. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Japan, September 2010.
  Details     BibTeX     Download: [pdf] (1.2MB )  [ps.gz] (3.2MB )  
• Jeff Bilmes. Shallow Thoughts on Deep Learning. Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Dec 2009. Slides for invited talk at NIPS 2009 Workshop “Deep Learning for Speech Recognition and Related Applications”, Li Deng, Dong Yu, Geoffrey E Hinton
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• Jeff Bilmes. Gaussian Models in Automatic Speech Recognition. In David Havelock, Sonoko Kuwano, and Michael Vorlander, editors, Handbook of Signal Processing in Acoustics, pp. 521–556, Springer Science+Business Media, LLC, 2008.
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• Jeff A. Bilmes. What HMMs Can Do. IEICE - Transactions on Information and Systems, E89-D(3):869–891, Oxford University Press, Oxford, UK, March 2006.
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• Jeff Bilmes, Jonathan Malkin, Xiao Li, Susumu Harada, Kelley Kilanski, Katrin Kirchhoff, Richard Wright, Amarnag Subramanya, James Landay, Patricia Dowden, and Howard Chizeck. The Vocal Joystick. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Toulouse, France, 2006.
  Details     BibTeX     Download: [pdf] (148.3kB )  [ps.gz] (186.3kB )  
• Jeff Bilmes and Marina Meila. Intransitive Classification and Choice. Technical Report UWEETR-2006-00021, University of Washington, Dept. of EE, 2006. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2006-0021.html
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2006-0021.html
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• Jeff Bilmes and Chris Bartels. Graphical Model Architectures for Speech Recognition. IEEE Signal Processing Magazine, 22(5):89–100, September 2005.
  Details     BibTeX     Download: [pdf] (1.6MB )  [ps.gz] (1.5MB )  
• Jeff A. Bilmes, Xiao Li, Jonathan Malkin, Kelley Kilanski, Richard Wright, Katrin Kirchhoff, Amarnag Subramanya, Susumu Harada, James A. Landay, Patricia Dowden, and Howard Chizeck. The Vocal Joystick: A Voice-Based Human-Computer Interface for Individuals with Motor Impairments (extended abstract). In 18th Annual ACM Sypm. on User Interface Software and Technology, Seattle, Oct 2005. Extended Abstract
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• Jeff A. Bilmes, Xiao Li, Jonathan Malkin, Kelley Kilanski, Richard Wright, Katrin Kirchhoff, Amarnag Subramanya, Susumu Harada, James A. Landay, Patricia Dowden, and Howard Chizeck. The Vocal Joystick: A Voice-Based Human-Computer Interface for Individuals with Motor Impairments. In Human Language Technology (HLT) Conference/Conference on Empirical Methods in Natural Language Processing (EMNLP), Vancouver, B.C., Oct 2005.
  Details     BibTeX     Download: [pdf] (360.2kB )  [ps.gz] (246.5kB )  
• Jeff Bilmes and Patrick Haffner. Tutorial: Machine learning in Speech and Language Processing. Tutorial presented during ICASSP, 2005, 2005.
  https://www.securecms.com/ICASSP2005/TutorialInfo.asp?TutorialID=13
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• Jeff Bilmes. What HMMs Can't Do: A Graphical Model Perspective. In Beyond HMM: Workshop on Statiatical Modeling Approach for Speech Recognition, Kyoto, Japan, December 2004. ATR Invited Paper and Lecture
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• Jeff Bilmes. On Soft Evidence in Bayesian Networks. Technical Report UWEETR-2004-0016, University of Washington, Dept. of Electrical Engineering, 2004. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2004-0016.html
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• Jeff Bilmes. Tutorial: Graphical Models in Speech and Language Research. Human Language Technology conference / North American chapter of the Association for Computational Linguistics(HLT/NAACL'04), 2004. https://melodi.ee.washington.edu/ bilmes/bilmes_hlt04_tutorial
  https://melodi.ee.washington.edu/ bilmes/bilmes_hlt04_tutorial
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• J. Bilmes and C. Bartels. On Triangulating Dynamic Graphical Models. In Uncertainty in Artificial Intelligence (UAI), pp. 47–56, Morgan Kaufmann Publishers, Acapulco, Mexico, 2003.
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• Jeff Bilmes and Katrin Kirchhoff. Factored Language Models and Generalized Parallel Backoff. In Human Language Technology Conference/North American chapter of the Association for Computational Linguistics (HLT/NAACL-2003), Edmonton, Alberta, May/June 2003.
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• J. Bilmes and K. Kirchhoff. Generalized Rules for Combination and Joint Training of Classifiers. Pattern Analysis and Applications, 6:201–211, 2003. Springer
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• Jeff Bilmes. Graphical Models and Automatic Speech Recognition. In R. Rosenfeld, M. Ostendorf, S. Khudanpur, and M. Johnson, editors, Mathematical Foundations of Speech and Language Processing, Springer-Verlag, New York, 2003.
  http://www.ima.umn.edu/pub/pub.html
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• Jeff Bilmes. Buried Markov Models: A Graphical Modeling Approach to Automatic Speech Recognition. Computer Speech and Language, 17(2--3):213–231, April--July 2003.
  Details     BibTeX     Download: (unavailable)
• Jeff Bilmes. Tutorial: Graphical Models Research in Audio, Speech, and Language Processing. Presented during the 2003 Uncertainty in Artificial Intelligence (UAI'03) conference, 2003. https://melodi.ee.washington.edu/ bilmes/uai03_tutorial
  https://melodi.ee.washington.edu/ bilmes/uai03_tutorial
  Details     BibTeX     Download: (unavailable)
• Jeff Bilmes and Geoff Zweig. The Graphical Models Toolkit: An Open Source Software System for Speech and Time-Series Processing. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, 2002.
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• J. Bilmes. The GMTK Documentation. 2002. \hrefhttp://melodi.ee.washington.edu/bilmes/gmtkhttp://melodi.ee.washington.edu/~bilmes/gmtk
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• Jeff Bilmes. Graphical Models and Automatic Speech Recognition. Technical Report UWEETR-2001-0005, University of Washington, Department of Electrical Engineering, 2001. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2001-0005.html
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• Jeff Bilmes. The Graphical Models Toolkit Documentation. Technical Documentation on the web, 2002.
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• Jeff Bilmes. What HMMs can do. Technical Report UWEETR-2002-0003, University of Washington, Department of Electrical Engineering, 2002. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2002-0003.html
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• Jeff Bilmes and Chris Bartels. On Triangulating Dynamic Graphical Models. Technical Report UWEETR-2002-0007, University of Washington, Department of Electrical Engineering, 2002. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2002-0007.html
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• Jeff Bilmes, Gang Ji, and Marina Meil\ua. Intransitive Likelihood-Ratio Classifiers. In Neural Information Processing Society (NeurIPS, formerly NIPS), 14, Vancouver, Canada, December 2001.
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• J. Bilmes, G. Zweig, T. Richardson, K. Filali, K. Livescu, P. Xu, K. Jackson, Y. Brandman, E. Sandness, E. Holtz, J. Torres, and B. Byrne. Discriminatively Structured Graphical Models for Speech Recognition: JHU-WS-2001 Final Workshop Report. Technical Report CLSP, Johns Hopkins University, Baltimore MD, 2001. http://www.clsp.jhu.edu/ws2001/groups/gmsr/GMRO-final-rpt.pdf
  http://www.clsp.jhu.edu/ws2001/groups/gmsr/GMRO-final-rpt.pdf
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• Jeff Bilmes and Katrin Kirchhoff. Directed Graphical Models of Classifier Combination: Application to Phone Recognition. In Proc. Int. Conf. on Spoken Language Processing, Beijing, China, October 2000.
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• Jeff Bilmes. Dynamic Bayesian Multinets. In Uncertainty in Artificial Intelligence (UAI), 16th, Morgan Kaufmann Publishers, 2000.
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• Jeff Bilmes. Factored Sparse Inverse Covariance Matrices. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, 2000.
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• Jeff Bilmes. Buried Markov Models for Speech Recognition. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Phoenix, AZ, March 1999.
  Details     BibTeX     Download: [pdf] (407.6kB )  [ps.gz] (57.0kB )  
• Jeff Bilmes. Natural Statistical Models for Automatic Speech Recognition. Ph.D. Thesis, U.C. Berkeley, Dept. of EECS, CS Division, 1999.
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• Jeff Bilmes. Data-Driven Extensions to HMM Statistical Dependencies. In Proc. Int. Conf. on Spoken Language Processing, Sidney, Australia, December 1998.
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• Jeff Bilmes. Maximum Mutual Information based reduction strategies for Cross-Correlation Based Joint Distributional Modeling. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Seattle, WA, May 1998.
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• Jeff Bilmes, Krste Asanovi\'c, CW Chin, and Jim Demmel. The PHiPAC v1.0 Matrix-Multiply Distribution. Technical Report 1020, Department of EECS, CS Division, University of California at Berkeley, 1998. http://www.icsi.berkeley.edu/~bilmes/phipac/
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• Jeff Bilmes. Joint Distributional Modeling with Cross-Correlation based Features. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Santa Barbara, December 1997.
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• Jeff Bilmes, Amin Vahdat, W. Hsu, and E.-J. Im.. Empirical Observations of Probabilistic Heuristics for the Clustering Problem. Technical Report ICSI-TR097-018, ICSI, 1997. ftp://ftp.icsi.berkeley.edu/pub/techreports/1997/tr-97-018.ps.gz
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• J. Bilmes, K. Asanovi\'c, C.-W. Chin, and J. Demmel. Using PHiPAC to speed Error Back-Propagation Learning. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, April 1997.
  Details     BibTeX     Download: [pdf] (154.9kB )  [ps.gz] (56.7kB )  [poster, pdf] (102.1kB )  
• J. Bilmes, K. Asanovi\'c, C.W. Chin, and J. Demmel. Optimizing Matrix Multiply using PHiPAC: a Portable, High-Performance, ANSI C Coding Methodology. In Proceedings of the International Conference on Supercomputing, ACM SIGARCH, Vienna, Austria, July 1997. Selected as one of the most influential ICS papers in 25 years, and inclusion in the special issue "25 years of International Conference on Supercomputing, 2014"
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• Jeff Bilmes. A Gentle Tutorial of the EM algorithm and its application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical Report TR-97-021, ICSI, 1997.
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• J. Bilmes, K. Asanovi\'c, J. Demmel, D. Lam, and C.W. Chin. PHiPAC: A Portable, High-Performance, ANSI C Coding Methodology and its application to Matrix Multiply. LAPACK Working Note 111 University of Tennessee, 1996.
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• J. Bilmes, N. Morgan, S.-L. Wu, and H. Bourlard. Stochastic Perceptual Speech Models With Durational Dependence. Intl. Conference on Spoken Language Processing, November 1996.
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• J. Bilmes. Temporal Gaussian Scaled Speech. \hrefhttp://www.icsi.berkeley.edu/ bilmes/earexhttp://www.icsi.berkeley.edu/~bilmes/earex, 1996. Contains audio examples of temporally Gaussian scaled speech.
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• J. Bilmes. Timing is of the Essence: Perceptual and Computational Techniques for Representing, Learning, and Reproducing Expressive Timing in Percussive Rhythm. Master's Thesis, MIT, Cambridge, MA,1993.
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• J. Bilmes. Techniques to Foster Drum Machine Expressivity. In International Computer Music Conference, 1993.
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• J. Bilmes. A Model for Musical Rhythm. In International Computer Music Conference, 1992.
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Bittremieux

• Wout Bittremieux, Damon H. May, Jeff Bilmes, and William Stafford Noble. A learned embedding for efficient joint analysis of millions of mass spectra.. Nature Methods, 2022. Code: https://bitbucket.org/noblelab/gleams/src/master/ and see https://doi.org/10.1038/s41592-022-01496-1
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Bulyko

• Ivan Bulyko, Mari Ostendorf, and Jeff Bilmes. Robust Splicing Costs and Efficient Search with BMM Models for Concatenative Speech Synthesis. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Orlando, Florida, 2002.
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Cetin

• Ozgur Cetin, Harriet Nock, Katrin Kirchhoff, Jeff Bilmes, and Mari Ostendorf. The 2001 GMTK-based SPINE ASR System. In Proc. Int. Conf. on Spoken Language Processing, Denver, Colorado, 2002.
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Chan

• Rachel CW Chan, Maxwell W Libbrecht, Eric G Roberts, Jeffrey A Bilmes, William Stafford Noble, and Michael M Hoffman. Segway 2.0: Gaussian Mixture Models and Minibatch Training. Bioinformatics, 34(4):669–671, Oxford University Press, 2017.
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Chen

• Xiaoyu Chen, Michael M. Hoffman, Jeff A. Bilmes, Jay R. Hesselberth, and William S. Noble. A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data. Bioinformatics, 26(12):i334–i342, 2010.
  http://dx.doi.org/10.1093/bioinformatics/btq175"> http://dx.doi.org/10.1093/bioinformatics/btq175
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• Chia-Ping Chen, Jeff Bilmes, and Dan Ellis. Speech Feature Smoothing for Robust ASR. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Philadelphia, PA, March 2005.
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• Chia-ping Chen, Jeff Bilmes, and Dan P. Ellis. Blind MVA Speech Feature Processing on Aurora 2.0. Technical Report UWEETR-2004-0017, University of Washington, Dept. of Electrical Engineering, 2004. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2004-0017.html
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• Chia-Ping Chen, Katrin Kirchhoff, and Jeff Bilmes. Towards Simple Methods of Noise-Robustness. Technical Report UWEETR-2002-0002, University of Washington, Dept. of EE, 2001. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2002-0002.html
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• Chia-Ping Chen, Karim Filali, and Jeff Bilmes. Frontend Post-Processing and Backend Model Enhancement on the Aurora 2.0/3.0 Databases. In Proc. Int. Conf. on Spoken Language Processing, Denver, Colorado, 2002.
  also see http://melodi.ee.washington.edu/people/chiaping/mva.html
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• Chia-Ping Chen and Jeff Bilmes. Low-Resource Noise-Robust Feature Post-Processing on Aurora 2.0. In Proc. Int. Conf. on Spoken Language Processing, Denver, Colorado, 2002.
  also see http://melodi.ee.washington.edu/people/chiaping/mva.html
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• Chia-Ping Chen, Katrin Kirchhoff, and Jeff Bilmes. Towards Simple Methods of Noise Robustness. Technical Report UWEETR-2002-0002, University of Washington, Department of Electrical Engineering, 2001. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2002-0002.html
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Chung

• Mike Chung, Eric Rombokas, Qi An, Yoky Matsuoka, and Jeff Bilmes. Continuous Vocalization Control Of A Full-Scale Assistive Robot. In 4th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob2012), Rome, Italy, June 2012.
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Consortium

• The ENCODE Project Consortium. An Integrated Encyclopedia of DNA Elements in the Human Genome. Nature, 489:57–74, September 2012.
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Cotter

• Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya Gupta, and Jeff Bilmes. Constrained Interacting Submodular Groupings. In International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.
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Das

• Arnav Mohanty Das, Gantavya Bhatt, Sahil Verma, Yiping Wang, Viswa Virinchi Muppirala, and Jeff Bilmes. Matched Data, Better Models: Target Aligned Data Filtering with Sparse Features. In International Conference on Learning Representations (ICLR-2026), 2026.
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• Arnav M. Das, Gantavya Bhatt, Lilly Kumari, Sahil Verma, and Jeff Bilmes. COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20534–20546, June 2025.
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• Arnav Das, Gantavya Bhatt, Lilly Kumari, Sahil Verma, and Jeff Bilmes. COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Learning. In Computer Vision and Pattern Recognition (CVPR), Music City Center, Nashville TN, June 2025.
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• Arnav Das, Gantavya Bhatt, Lilly Kumari, Sahil Verma, and Jeff Bilmes. COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Learning. In Proceedings of the ICML Workshop on Data-Centric Machine Learning Research, 2024.
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• Arnav Mohanty Das, Gantavya Bhatt, Megh Manoj Bhalerao, Vianne R. Gao, Rui Yang, and Jeff Bilmes. Accelerating Batch Active Learning Using Continual Learning Techniques. Transactions on Machine Learning Research, 2023.
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• Arnav Mohanty Das, Gantavya Bhatt, Megh Manoj Bhalerao, Vianne R. Gao, Rui Yang, and Jeff Bilmes. Accelerating Batch Active Learning Using Continual Learning Techniques (arxiv). Transactions on Machine Learning Research, 2023.
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• Arnav Das, Rui Yang, Vianne Gao, Alireza Karbalaghareh, William Noble, Jeff Bilmes, and Christina Leslie. Epiphany: Predicting the Hi-C Contact Map from 1D Epigenomic Data. In The 2021 ICML Workshop on Computational Biology, Virtual, July 2021.
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Dolhansky

• Brian Dolhansky and Jeff Bilmes. Deep Submodular Functions: Definitions and Learning. In Neural Information Processing Society (NeurIPS, formerly NIPS), Barcelona, Spain, December 2016.
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Durham

• Timothy J Durham, Maxwell W Libbrecht, J Jeffry Howbert, Jeff Bilmes, and William Stafford Noble. PREDICTD parallel epigenomics data imputation with cloud-based tensor decomposition. Nature communications, 9(1):1402, Nature Publishing Group, 2018.
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• Timothy J Durham, Maxwell W Libbrecht, J Jeffry Howbert, Jeffrey Bilmes, and William S Noble. PREDICTD: PaRallel Epigenomics Data Imputation With Cloud-based Tensor Decomposition. bioRxiv, Cold Spring Harbor Laboratory, 2017.
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Elliott

• Cameron Elliott and Jeff Bilmes. Computer Based Mathematics Using Continuous Speech Recognition. In Striking a Chord: Vocal Interaction in Assistive Technologies, Games, and More: CHI 2007 workshop on non-verbal acoustic interaction, San Jose, CA, April 2007.
  http://melodi.ee.washington.edu/ camkego/cammath/
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Ellis

• Dan Ellis and Jeff Bilmes. Using Mutual Information to Design Feature Combinations. In Proc. Int. Conf. on Spoken Language Processing, Beijing, China, October 2000.
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Everitt

• Katherine Everitt, Susumu Harada, Jeff Bilmes, and James Landay. Disambiguating speech commands using physical context. In 9th ACM International Conference on Multimodal Interfaces (ICMI07), pp. 247–254, ACM, New York, NY, USA, November 12-15 2007.
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Filali

• Karim Filali and Jeff Bilmes. Generalized Graphical Abstractions for Statistical Machine Translation. In Human Language Technology Conference/North American chapter of the Association for Computational Linguistics (HLT/NAACL-2007), Rochester, NY, April 2007.
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• Karim Filali and Jeff Bilmes. Multi-dynamic Bayesian Networks. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2006.
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• Karim Filali and Jeff Bilmes. Leveraging Multiple Languages to Improve Statistical MT Word Alignments. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Cancun, Mexico, Nov/Dec 2005.
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• Karim Filali and Jeff Bilmes. A Dynamic Bayesian Framework to Model Context and Memory in Edit Distance Learning: An Application to Pronunciation Classification. In Proceedings of the Association for Computational Linguistics (ACL), 43, University of Michigan, Ann Arbor, 2005.
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• Karim Filali, Xiao Li, and Jeff Bilmes. Algorithms for Data-driven ASR Parameter Quantization. Computer Speech and Language, 20(4):625–643, October 2005.
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• Karim Filali, Xiao Li, and Jeff Bilmes. Algorithms for Data-Driven ASR Parameter Quantization. Technical Report UWEETR-2003-0010, University of Washington, Dept. of Electrical Engineering, 2003. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2003-0010.html
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• Karim Filali, Xiao Li, and Jeff Bilmes. Data-Driven Vector Clustering for Low-Memory Footprint ASR. In Proc. Int. Conf. on Spoken Language Processing, Denver, Colorado, 2002.
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Gao

• Vianne R Gao, Rui Yang, Arnav Das, Renhe Luo, Hanzhi Luo, Dylan R McNally, Ioannis Karagiannidis, Martin A Rivas, Zhong-Min Wang, Darko Barisic, and others. ChromaFold predicts the 3D contact map from single-cell chromatin accessibility. Nature Communications, 15(1):9432, Nature Publishing Group UK London, 2024.
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Gillenwater

• Jennifer Gillenwater, Rishabh Iyer, Bethany Lusch, Rahul Kidambi, and Jeff Bilmes. Submodular Hamming Metrics. In Neural Information Processing Society (NeurIPS, formerly NIPS), Montreal, Canada, December 2015.
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Gowdy

• John Gowdy, Amar Subramanya, Chris Bartels, and Jeff Bilmes. DBN-Based Multi-Stream Models for Audio-Visual Speech Recognition. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Montreal, Canada, May 2004.
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Guillory

• Andrew Guillory and Jeff Bilmes. Online Submodular Set Cover, Ranking, and Repeated Active Learning. In Neural Information Processing Society (NeurIPS, formerly NIPS), Granada, Spain, December 2011.
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• Andrew Guillory and Jeff Bilmes. Active Semi-Supervised Learning using Submodular Functions. In Uncertainty in Artificial Intelligence (UAI), AUAI, Barcelona, Spain, July 2011.
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• Andrew Guillory and Jeff A. Bilmes. Simultaneous Learning and Covering with Adversarial Noise. In International Conference on Machine Learning (ICML), Bellevue, Washington, 2011.
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• Andrew Guillory and Jeff A. Bilmes. Simultaneous Learning and Covering with Adversarial Noise. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Vancouver, Canada, December 2010. NeurIPS (formerly NIPS) Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML)
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• Andrew Guillory and Jeff Bilmes. Interactive Submodular Set Cover. In International Conference on Machine Learning (ICML), Haifa, Israel, 2010.
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• Andrew Guillory and Jeff Bilmes. Interactive Submodular Set Cover. Technical Report UWEETR-2010-0001, University of Washington, Dept. of EE, 2010.
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2010-0001.html
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• Andrew Guillory and Jeff Bilmes. Label Selection on Graphs. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2009.
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• Andrew Guillory and Jeff Bilmes. Average-Case Active Learning with Costs. In The 20th International Conference on Algorithmic Learning Theory, University of Porto, Portugal, October 2009.
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• Andrew Guillory and Jeff Bilmes. Average-Case Active Learning with Costs. Technical Report UWEETR-2009-0005, University of Washington, Dept. of EE, 2009.
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2009-0005.html
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• Andrew Guillory, Erick Chastain, and Jeff Bilmes. Active Learning as Non-Convex Optimization. In Twelfth International Conference on Artificial Intelligence and Statistics (AISTAT), Clearwater Beach, Florida, April 2009.
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• Andrew Guillory and Jeff Bilmes. Practical Methods for Exploiting Bounds on Change in the Margin. In The Tenth International Symposium on Artificial Intelligence and Mathematics (ISAIM 2007), Fort Lauderdale, Florida, 2008.
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Gunther

• Oliver Gunther and Jeff Bilmes. Tree-Based Access Methods for Spatial Databases: Implementation and Performance Evaluation. IEEE Transactions on Knowledge and Data Engineering, 3(3), 1991.
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• Oliver Gunther and Jeff Bilmes. The Implementation of the Cell Tree: Design Alternatives and Performance Evaluation. Datenbanksysteme in Buro, Technik und Wissenschaft, Informatik-Fachberichte, 204, 1989. Springer-Verlag
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Halloran

• John Halloran, Jeff Bilmes, and William Noble. A dynamic Bayesian network for accurate detection of peptides from tandem mass spectra. Journal of Proteome Research, ACS Publications, 2016.
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• John Halloran, Jeff A. Bilmes, and William S. Noble. Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. In Uncertainty in Artificial Intelligence (UAI), AUAI, Quebic City, Quebec Canada, July 2014.
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Harada

• Susumu Harada, Jacob O. Wobbrock, Jonathan Malkin, Jeff A. Bilmes, and James A. Landay. Longitudinal Study of People Learning to Use Continuous Voice-Based Cursor Control. In CHI 2008: ACM Conference on Human Factors in Computing Systems, Boston, MA, April 2009.
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• Susumu Harada, James Landay, Jon Malkin, Xiao Li, and Jeff Bilmes. The Vocal Joystick: evaluation of voice-based cursor control techniques for assistive technology. Disability and Rehabilitation: Assistive Technology, 3(1):22–34, January 2008.
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• Susumu Harada, James A. Landay, and Jeff Bilmes. Drawing with Voice: Combining Non-Verbal Vocalizations with Words to Draw Hands-Free. In Striking a Chord: Vocal Interaction in Assistive Technologies, Games, and More: CHI 2007 workshop on non-verbal acoustic interaction, San Jose, CA, April 2007.
  http://www.vocal-input.org
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• Susumu Harada, James Landay, Jon Malkin, Xiao Li, and Jeff Bilmes. The Vocal Joystick: Evaluation of Voice-based Cursor Control Techniques. In Proceedings of the 8th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS06), pp. 197–204, ACM, Portland, Oregon, October 2006. New York, NY
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Hoffman

• M. M. Hoffman, J. Ernst, S. P. Wilder, A. Kundaje, R. S. Harris, M. Libbrecht, B. Giardine, P. M. Ellenbogen, J. A. Bilmes, E. Birney, R. C. Hardison, I. Dunham, M. Kellis, and W. S. Noble. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res, 41(2):827–41, 2013.
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• Michael M Hoffman, Orion J Buske, Jie Wang, Zhiping Weng, Jeff A Bilmes, and William Stafford Noble. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nature Methods, 9(5):473–476, March 2012.
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House

• Brandi House, Jonathan Malkin, and Jeff A. Bilmes. The VoiceBot: A Voice Controlled Robot Arm. In CHI 2009: ACM Conference on Human Factors in Computing Systems, Boston, MA, April 2009.
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Hristov

• Borislav H Hristov, Jeffrey A Bilmes, and William Stafford Noble. Linking cells across single-cell modalities by synergistic matching of neighborhood structure. Bioinformatics, 38(Supplement_2):ii148–ii154, 09 2022.
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• Borislav Hristov, Jeffrey A. Bilmes, and William Stafford Noble. Linking cells across single-cell modalities by synergistic matching of neighborhood structure (TR).. bioRxiv, 2022. Code: https://github.com/Noble-Lab/synmatch
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Hu

• Alex Hu, Yang Young Lu, Jeff Bilmes, and William Stafford Noble. Joint precursor elution profile inference via regression for peptide detection in data-independent acquisition mass spectra. Journal of proteome research, 18(1):86–94, ACS Publications, 2019.
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Iyer

• Rishabh Iyer, Abir De, Ganesh Ramakrishnan, and Jeff Bilmes. Subset Selection in Machine Learning: Theory, Applications, and Hands On. Thirty-Sixth Conference on Artificial Intelligence, AAAI-2022 Tutorial Forum, 2022. AAAI-22 tutorials url: https://aaai.org/Conferences/AAAI-22/aaai22tutorials/
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• Rishabh Iyer, Ninad Khargonkar, Jeff Bilmes, and Himanshu Asnani. Generalized Submodular Information Measures: Theoretical Properties, Examples, Optimization Algorithms, and Applications. IEEE Transactions on Information Theory, 68(2):752 – 781, February 2022.
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• Rishabh Iyer, Ninad A Khargonkar, Jeffrey A. Bilmes, and Himanshu Asnani. Submodular Combinatorial Information Measures with Applications in Machine Learning. In The 32nd International Conference on Algorithmic Learning Theory, Virtual Conference, March 2021.
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• Rishabh Iyer and Jeff Bilmes. Concave Aspects of Submodular Functions. In IEEE International Symposium on Information Theory, June 2020.
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• Rishabh Iyer and Jeffrey Bilmes. Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs. In 22nd International Conference on Artificial Intelligence and Statistics (AISTATS-2019), April 2019.
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• Rishabh Iyer and Jeffrey Bilmes. A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems. In 22nd International Conference on Artificial Intelligence and Statistics (AISTATS-2019), April 2019.
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• Rishabh K. Iyer and Jeff A. Bilmes. Polyhedral aspects of Submodularity, Convexity and Concavity. Arxiv, CoRR, abs/1506.07329, 2015.
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• Rishabh Iyer and Jeff Bilmes. Submodular Point Processes. In 18th International Conference on Artificial Intelligence and Statistics (AISTATS-2015), May 2015.
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• Rishabh Iyer, Stefanie Jegelka, and Jeff Bilmes. Monotone Closure of Relaxed Constraints in Submodular Optimization:Connections Between Minimization and Maximization. In Uncertainty in Artificial Intelligence (UAI), AUAI, Quebic City, Quebec Canada, July 2014.
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• Rishabh Iyer and Jeff Bilmes. Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints. In Neural Information Processing Society (NeurIPS, formerly NIPS), Lake Tahoe, CA, December 2013. Winner of Outstanding Paper Award, NIPS-2013
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• Rishabh Iyer and Jeff Bilmes. Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints: Extended Version. arXiv, 2013. extended version of NIPS-2013
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• Rishabh Iyer, Stefanie Jegelka, and Jeff Bilmes. Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. In Neural Information Processing Society (NeurIPS, formerly NIPS), Lake Tahoe, CA, December 2013.
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• Rishabh Iyer, Stefanie Jegelka, and Jeff Bilmes. Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. arXiv, 2013. extended version of NIPS-2013
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• Rishabh Iyer and Jeff Bilmes. The Lovász-Bregman Divergence and connections to rank aggregation, clustering and web ranking. In Uncertainty in Artificial Intelligence (UAI), AUAI, Bellevue, Washington, July 2013.
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• Rishabh Iyer and Jeff Bilmes. The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking. arXiv, 2013. extended version of UAI-2013
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• Rishabh Iyer, Stefanie Jegelka, and Jeff Bilmes. Fast Semidifferential-based Submodular Function Optimization. arXiv, 2013. extended version of ICML-2013
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• Rishabh Iyer, Stefanie Jegelka, and Jeff A. Bilmes. Fast Semidifferential-based Submodular Function Optimization. In International Conference on Machine Learning (ICML), Atlanta, Georgia, 2013. Winner of the Best Paper Award, ICML 2013
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• Rishabh Iyer and Jeff Bilmes. Submodular Bregman Divergences with Applications. In Neural Information Processing Society (NeurIPS, formerly NIPS), Lake Tahoe, CA, December 2012.
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• Rishabh Iyer and Jeff Bilmes. Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications. In Uncertainty in Artificial Intelligence (UAI), AUAI, Catalina Island, USA, July 2012.
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• Rishabh Iyer and Jeff Bilmes. Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications. arXiv, 2012. extended version of UAI-2012
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• Vijay Iyer, Jeff Bilmes, Matt Wright, and David Wessel. Representing groove: Rhythmic structure in interactive music performance. The Journal of the Acoustical Society of America, 102(5):3182–3182, 1997.
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• Vijay Iyer, Jeff Bilmes, Matt Wright, and David Wessel. A Novel Representation for Rhythmic Structure. In International Computer Music Conference, September 1997.
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Jegelka

• Stefanie Jegelka and Jeff Bilmes. Graph cuts with interacting edge weights: examples, approximations, and algorithms. Mathematical Programming, pp. 1–42, 2016.
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• Stefanie Jegelka and Jeff A. Bilmes. Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms. arXiv, 2014.
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• Stefanie Jegelka, Hui Lin, and Jeff A. Bilmes. Fast Approximate Submodular Minimization. In Neural Information Processing Society (NeurIPS, formerly NIPS), Granada, Spain, December 2011.
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• Stefanie Jegelka and Jeff A. Bilmes. Approximation Bounds for Inference using Cooperative Cuts. In International Conference on Machine Learning (ICML), Bellevue, Washington, 2011.
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• Stefanie Jegelka and Jeff A. Bilmes. Online Submodular Minimization for Combinatorial Structures. In International Conference on Machine Learning (ICML), Bellevue, Washington, 2011.
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• Stefanie Jegelka and Jeff A. Bilmes. Submodularity beyond submodular energies: coupling edges in graph cuts. In Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 2011.
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• Stefanie Jegelka and Jeff A. Bilmes. Multi-label Cooperative Cuts. In CVPR 2011 Workshop on Inference in Graphical Models with Structured Potentials, Colorado Springs, CO, June 2011.
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• Stefanie Jegelka and Jeff A. Bilmes. Online Algorithms for Submodular Minimization with Combinatorial Constraints. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Vancouver, Canada, December 2010. NeurIPS (formerly NIPS) Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML)
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• Stefanie Jegelka and Jeff Bilmes. Cooperative Cuts for Image Segmentation. Technical Report UWEETR-2010-0003, University of Washington, Seattle, 2010.
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2010-0003.html
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• Stefanie Jegelka and Jeff Bilmes. Cooperative Cuts: Graph Cuts with Submodular Edge Weights. In 24th European Conference on Operational Research (EURO XXIV), Lisbon, Portugal, 2010.
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• Stefanie Jegelka and Jeff Bilmes. Cooperative Cuts: Graph Cuts with Submodular Edge Weights. Technical Report 189-03-2010, Max Planck Institute for Biological Cybernetics, Tuebingen, 2010.
  http://www.kyb.mpg.de/publication.html?publ=6330
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• Stefanie Jegelka and Jeff A. Bilmes. Notes on graph cuts with submodular edge weights. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Vancouver, Canada, December 2009. NeurIPS (formerly NIPS) Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML)
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Ji

• Gang Ji and Jeff Bilmes. Jointly Recognizing Multi-Speaker Conversations. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Dallas, Texas, 2010.
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• Gang Ji, Jeff Bilmes, Jeff Michels, Katrin Kirchhoff, and Chris Manning. Graphical Model Representations of Word Lattices. In IEEE/ACL 2006 Workshop on Spoken Language Technology (SLT2006), Palm Beach, Aruba, Dec 2006.
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• Gang Ji and Jeff Bilmes. Backoff Model Training using Partially Observed Data: Application to Dialog Act Tagging. In Human Language Technology Conference/North American chapter of the Association for Computational Linguistics (HLT/NAACL-2006), New York, NY, June 2006.
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• Gang Ji and Jeff Bilmes. Dialog Act Tagging using Graphical Models. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Philadelphia, PA, March 2005.
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• Gang Ji and Jeff Bilmes. Backoff Model Training using Partially Observed Data: Application to Dialog Act Tagging. Technical Report UWEETR-2005-0008, University of Washington, Dept. of EE, 2005. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2005-0008.html
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2005-0008.html
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• Gang Ji and Jeff Bilmes. Multi-Speaker Language Modeling. In Human Language Technology Conference/North American chapter of the Association for Computational Linguistics (HLT/NAACL-2004), Boston, MA, May 2004.
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• Gang Ji and Jeff Bilmes. Necessary Intransitive Likelihood-Ratio Classifiers. In Neural Information Processing Society (NeurIPS, formerly NIPS), 16, Vancouver, Canada, December 2003.
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• Gang Ji and Jeff Bilmes. Necessary Intransitive Likelihood-Ratio Classifiers. Technical Report UWEETR-2002-0014, University of Washington, Dept. of EE, 2002. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2002-0014.html
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Kawahara

• Yoshinobu Kawahara, Rishabh Iyer, and Jeff Bilmes. On Approximate Non-submodular Minimization via Tree-Structured Supermodularity. In 18th International Conference on Artificial Intelligence and Statistics (AISTATS-2015), May 2015.
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• Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, and Jeff Bilmes. Submodularity Cuts and Applications. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2009.
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Kilanski

• Kelley Kilanski, Jonathan Malkin, Xiao Li, Richard Wright, and Jeff Bilmes. The Vocal Joystick Data Collection Effort and Vowel Corpus. In Proc. Int. Conf. on Spoken Language Processing, Pittsburg, Pa., September 2006.
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King

• Simon King, Chris Bartels, and Jeff Bilmes. SVitchboard 1: Small vocabulary tasks from Switchboard 1. In European Conf. on Speech Communication and Technology (Eurospeech), Lisbon, Portugal, September 2005.
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Kirchhoff

• Katrin Kirchhoff and Jeff Bilmes. Submodularity for Data Selection in Machine Translation. In Empirical Methods in Natural Language Processing (EMNLP), October 2014.
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• Katrin Kirchhoff, Yuzong Liu, and Jeff Bilmes. Classification of Developmental Disorders from Speech Signals Using Submodular Feature Selection. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Lyon, France, August 2013.
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• Katrin Kirchhoff, Jeff Bilmes, and Kevin Duh. Factored Language Models Tutorial. Technical Report UWEETR-2008-00048, University of Washington, Dept. of EE, 2008. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2008-0004.html
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2008-0004.html
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• Katrin Kirchhoff, Jeff Bilmes, Sourin Das, Nicolae Duta, Melissa Egan, Gang Ji, Feng He, John Henderson, Daben Liu, Mohamed Noamany, Pat Schone, Richard Schwartz, and Dimitra Vergyri. Novel Approaches to Arabic Speech Recognition: Report from the 2002 Johns-Hopkins Summer Workshop. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Hong Kong, China, May 2003.
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• Katrin Kirchhoff, Sonia Parandekar, and Jeff Bilmes. Mixed-memory Markov models for Automatic Language Identification. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Orlando, Florida, 2002.
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• Katrin Kirchhoff and Jeff Bilmes. Combination and Joint Training of Acoustic Classifiers for Speech Recognition. In Proc. of the ISCA ITRW Automatic Speech Recognition 2000 Workshop, Paris, France, October 2000.
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• Katrin Kirchhoff and Jeff Bilmes. Dynamic classifier combination in hybrid speech recognition systems using utterance-level confidence values. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, pp. 693–696, 1999.
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• Katrin Kirchhoff and Jeff Bilmes. Statistical Acoustic Indications of Coarticulation. In Proceedings 14th International Congress of Phonetic Sciences, august 1999.
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Klammer

• Aaron Klammer, Sheila Reynolds, Jeff Bilmes, Mike MacCoss, and William Noble. Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification. Bioinformatics, 24(13):348–56, July 2008.
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• Aaron A. Klammer, Sheila M. Reynolds, Jeff A. Bilmes, Michael J. MacCoss, and William Noble. Modelling peptide fragmentation with dynamic Bayesian networks for peptide identification. In 16th Annual International Conference on Intelligent Systems forMolecular Biology (Bioinformatics, Proceedings of the ISMB), Toronto, July 2008.
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Kohn

• P. Kohn, J. Bilmes, N. Morgan, and J. Beck. Software for ANN training on a Ring Array Processor. In Neural Information Processing Society (NeurIPS, formerly NIPS), 4, May 1991.
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Kothawade

• Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, and Rishabh Iyer. Prism: A rich class of parameterized submodular information measures for guided subset selection. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10238–10246, 36, 2022.
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Kumaran

• Raghunandan Kumaran, Jeff Bilmes, and Katrin Kirchhoff. Attention Shift Decoding for Conversational Speech Recognition. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Antwerp, Belgium, September 2007.
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Kumari

• Lilly Kumari, Shengjie Wang, Tianyi Zhou, Nikhil Sarda, Anthony Rowe, and Jeff Bilmes. BumbleBee: Dynamic KV-Cache Streaming Submodular Summarization for Infinite-Context Transformers. In First Conference on Language Modeling, Seattle, WA, 2024. Published as a conference paper at COLM 2024
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• Lilly Kumari, Shengjie Wang, Arnav Das, Tianyi Zhou, and Jeff Bilmes. An End-to-End Submodular Framework for Data-Efficient In-Context Learning. In Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Mexico City, Mexico, June 16--21 2024.
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• Lilly Kumari, Shengjie Wang, Tianyi Zhou, and Jeff A Bilmes. Retrospective Adversarial Replay for Continual Learning. In Neural Information Processing Society (NeurIPS, formerly NIPS), pp. 28530–28544, 35, New Orleans, Louisiana, December 2022.
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• Lilly Kumari and Jeff Bilmes. Submodular Span, with Applications to Conditional Data Summarization. In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21),, A Virtual Conference, February 2021.
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Lavania

• Chandrashekhar Lavania, Kai Wei, Rishabh Iyer, and Jeff Bilmes. A Practical Online Framework with a Fixed Memory Budget for Extracting Running Video Summaries. In SIAM International Conference on Data Mining (SDM-2021), April 2021. Video demo: https://youtu.be/lTzLWGcb8Mg
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• Chandrashekhar Lavania and Jeff Bilmes. Auto-Summarization: A Step Towards Unsupervised Learning Of a Submodular Mixture. In SIAM International Conference on Data Mining (SDM-2019), May 2019.
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• Chandrashekhar Lavania and Jeff Bilmes. Reducing Total Latency in Online Real-time inference and Decoding via Combined context window and model smoothing latencies. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, New Orleans, USA, 2017.
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• Chandrashekhar Lavania, Sunil Thulasidasan, Anthony Lamarca, Jeff Scofield, and Jeff Bilmes. A Weakly Supervised Online Activity Recognition Framework forReal-time Synthetic Biology Laboratory Assistance. In 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), Heidelberg, Germany, September 2016.
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Lei

• Xin Lei, Gang Ji, Tim Ng, Jeff Bilmes, and Mari Ostendorf. DBN-Based Multi-stream Models for Mandarin Toneme Recognition. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Philadelphia, PA, March 2005.
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Li

• Tian Li, Tianyi Zhou, and Jeff Bilmes. Tilted Sharpness-Aware Minimization. In International Conference on Machine Learning (ICML), July 2025.
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• Xiao Li and Jeff Bilmes. A Bayesian Divergence Prior for Classifier Adaptation. In Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007), March 2007.
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• Xiao Li, Jonathan Malkin, Susumu Harada, Jeff Bilmes, Richard Wright, and James Landay. An Online Adaptive Filtering Algorithm for the Vocal Joystick. In Proc. Int. Conf. on Spoken Language Processing, Pittsburg, Pa., September 2006.
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• Xiao Li, Gang Ji, and Jeff Bilmes. A Factored Language Model for Quantized Music. In International Conference on Computer Music (ICMC06), New Orleans, LA., Oct 2006.
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• Xiao Li and Jeff Bilmes. Regularized Adaptation of Discriminative Classifiers. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Toulouse, France, May 2006.
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• Xiao Li and Jeff Bilmes. A Divergence Prior for Adaptive Learning. In NIPS 2006 Workshop; Learning When Test and Training Inputs Have Different Distributions, December 2006.
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• Xiao Li, Jon Malkin, and Jeff Bilmes. A High-speed, Low-Resource ASR Back-end Based on Custom Arithmetic. IEEE Trans. Speech and Audio Processing, 14(5):1683–1693, 2006.
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• Yi Li, Linda Shapiro, and Jeff Bilmes. A Generative/Discriminative Learning Algorithm for Object Recognition. In 10th IEEE Conference on Computer Vision (ICCV), Beijing, China, 2005.
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• Xiao Li, Jeff Bilmes, and Jon Malkin. Maximum Margin Learning and Adaptation of MLP Classifers. In European Conf. on Speech Communication and Technology (Eurospeech), Lisbon, Portugal, September 2005.
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• Xiao Li and Jeff Bilmes. Feature Pruning for Low-Power ASR Systems in Clean and Noisy Environments. IEEE Signal Processing Letters, pp. 489–492, June 2005.
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• Xiao Li, Jon Malkin, and Jeff Bilmes. A graphical Model Approach to Pitch Tracking. In Proc. Int. Conf. on Spoken Language Processing, Jeju Island, Korea, October 2004.
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• Yi Li, Jeff A. Bilmes, and Linda G. Shapiro. Object Class Recognition using Images of Abstract Regions. In International Conference on Pattern Recognition, Cambridge, UK, August 2004.
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• Xiao Li, Jonathan Malkin, and Jeff Bilmes. Codebook Design for ASR Systems using Custom Arithmetic Units. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Montreal, Canada, May 2004.
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• Xiao Li and Jeff Bilmes. Feature Pruning in Likelihood Evaluation of HMM-based Speech Recognition. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), St. Thomas, U.S. Virgin Islands, Nov/Dec 2003.
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• Xiao Li and Jeff Bilmes. Selectively Computing Dynamic Features in the Likelihood Computation of ASR Systems. Technical Report UWEETR-2003-0009, University of Washington, Department of Electrical Engineering, 2003. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2003-0009.html
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Libbrecht

• Maxwell W. Libbrecht, Jeffrey A. Bilmes, and William Stafford Noble. Choosing non-redundant representative subsets of protein sequence data sets using submodular optimization. Proteins: Structure, Function, and Bioinformatics, 86(4):454–466, 2018.
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• Maxwell W. Libbrecht, Oscar Rodriguez, Zhiping Weng, Jeffrey A. Bilmes, Michael M. Hoffman, and William Stafford Noble. A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types.. bioRxiv, 2018.
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• Maxwell W Libbrecht, Jeffrey A Bilmes, and William Stafford Noble. Eliminating redundancy among protein sequences using submodular optimization. bioRxiv, Cold Spring Harbor Labs Journals, 2016.
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• Maxwell Libbrecht, Michael Hoffman, William Noble, and Jeff Bilmes. Entropic Graph-based Posterior Regularization. In International Conference on Machine Learning (ICML), Lille, France, 2015.
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• Maxwell W Libbrecht, Ferhat Ay, Michael M Hoffman, David M Gilbert, Jeffrey A Bilmes, and William Stafford Noble. Joint Annotation of Chromatin State and Chromatin Conformation Reveals Relationships Among Domain Types and Identifies Domains of Cell Type-Specific Expression. Genome Research, Cold Spring Harbor Labs Journals, 2015. https://www.iscb.org/recomb-regsysgen2015-submissions/recomb-regsysgen2015-top-papers-reading-papers Selected as a 2014 - 2015 Top Paper at RSG 2015
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• Maxwell Libbrecht, Michael Hoffman, William Noble, and Jeffrey Bilmes. Entropic Graph-based Posterior Regularization for Learning Probabilistic Models. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Lake Tahoe, Nevada, December 2013. NIPS Workshop on Machine Learning in Computational Biology (MLCB)
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Lin

• Andy Lin, Brooke L Deatherage Kaiser, Janine R Hutchison, Jeffrey A Bilmes, and William Stafford Noble. MS1Connect: a mass spectrometry run similarity measure. Bioinformatics, 39(2), 01 2023. btad058
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• Andy Lin, Brooke L. Deatherage Kaiser, Janine R. Hutchison, Jeffrey A. Bilmes, and William Stafford Noble. MS1Connect: a mass spectrometry run similarity measure. bioRxiv, 2022. Code: https://github.com/bmx8177/MS1Connect
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• Hui Lin and Jeff Bilmes. Learning Mixtures of Submodular Shells with Application to Document Summarization. In Uncertainty in Artificial Intelligence (UAI), AUAI, Catalina Island, USA, July 2012.
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• Hui Lin and Jeff A. Bilmes. Optimal Selection of Limited Vocabulary Speech Corpora. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Florence, Italy, August 2011.
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• Hui Lin and Jeff Bilmes. A Class of Submodular Functions for Document Summarization. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL/HLT-2011), Portland, OR, June 2011. (long paper)
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• Hui Lin and Jeff Bilmes. Word Alignment via Submodular Maximization over Matroids. In North American chapter of the Association for Computational Linguistics/Human Language Technology Conference (NAACL/HLT-2011), Portland, OR, June 2011. (short paper)
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• Hui Lin and Jeff A. Bilmes. An Application of the Submodular Principal Partition to Training Data Subset Selection. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Vancouver, Canada, December 2010. NeurIPS (formerly NIPS) Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML)
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• Hui Lin and Jeff Bilmes. Multi-document Summarization via Budgeted Maximization of Submodular Functions. In North American chapter of the Association for Computational Linguistics/Human Language Technology Conference (NAACL/HLT-2010), Los Angeles, CA, June 2010.
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• Hui Lin, Jeff Bilmes, and Shasha Xie. Graph-based Submodular Selection for Extractive Summarization. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Merano, Italy, December 2009.
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• Hui Lin and Jeff A. Bilmes. How to Select a Good Training-data Subset for Transcription: Submodular Active Selection for Sequences. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brighton, UK, September 2009.
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• Hui Lin, Koby Crammer, and Jeff Bilmes. How to Lose Confidence: Probabilistic Linear Machines for Multiclass Classification. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brighton, UK, September 2009.
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• Hui Lin, Alex Stupakov, and Jeff Bilmes. Improving Multi-Lattice-Alignment Based Spoken Keyword Spotting. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 2009.
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• Hui Lin, Alex Stupakov, and Jeff Bilmes. Spoken Keyword Spotting via Multi-Lattice Alignment. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brisbane, Australia, September 2008.
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• Hui Lin and Jeff Bilmes. Polyphase Speech Recognition. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Las Vegas, NV, April 2008.
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• Hui Lin, Jeff Bilmes, Dimitra Vergyri, and Katrin Kirchhoff. OOV Detection by Joint Word/Phone Lattice Alignment. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Kyoto, Japan, December 2007.
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Liu

• Yuzong Liu, Rishabh Iyer, Katrin Kirchhoff, and Jeff Bilmes. SVitchboard-II and FiSVer-I: Crafting high quality and low complexity conversational english speech corpora using submodular function optimization. Computer Speech & Language, 42:122–142, 2017.
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• Jie Liu, John Halloran, Jeffrey Bilmes, Riza Daza, Choli Lee, Elisabeth Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael Dorschner, Vijayakrishna Gadi, Jay Shendure, Anthony Blau, and William Noble. Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies. bioRxiv, Cold Spring Harbor Laboratory, 2017.
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• Yuzong Liu, Rishabh Iyer, Katrin Kirchhoff, and Jeff Bilmes. SVitchboard II and FiSVer I: High-Quality Limited-Complexity Corpora of Conversational English Speech. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Dresden, Germany, September 2015.
  Details     BibTeX     Download: [pdf] (184.8kB )  [ps.gz] (210.8kB )  
• Yuzong Liu, Kai Wei, Katrin Kirchhoff, Yisong Song, and Jeff Bilmes. Submodular Feature Selection For High-Dimensional Acoustic Score Spaces. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Vancouver, Canada, 2013.
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Livescu

• Karen Livescu, James Glass, and Jeff Bilmes. Hidden Feature Models for Speech Recognition Using Dynamic Bayesian Networks. In European Conf. on Speech Communication and Technology (Eurospeech), 8th, Geneva, Switzerland, 2003.
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Lu

• Yang Young Lu, Jeff Bilmes, Ricard A. Rodriguez-Mias, Judit Villen, and William Stafford Noble. DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data. Bioinformatics (Proceedings of the ISMB), 2022. Code: https://bitbucket.org/noblelab/diameter/src/master/ and see https://doi.org/10.1093/bioinformatics/btab284
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• Yang Young Lu, Jeff Bilmes, Ricard A Rodriguez-Mias, Judit Villén, and William Stafford Noble. DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data. bioRxiv, Cold Spring Harbor Laboratory, 2021.
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Ma

• Ning Ma, Chris D. Bartels, Jeff A. Bilmes, and Phil D. Green. Modelling the Prepausal Lengthening Effect for Speech Recognition: A Dynamic Bayesian Network Approach. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 2009.
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Malkin

• Jonathan Malkin and Jeff A. Bilmes. Using Semi-Supervised Learning to Smooth Class Transitions. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Japan, September 2010.
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• Jonathan Malkin, Xiao Li, Susumu Harada, James Landay, and Jeff Bilmes. The Vocal Joystick Engine v1.0. Computer Speech and Language (accepted, to appear), -(-):–, --- 2010.
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• Jonathan Malkin, Amar Subramanya, and Jeff A. Bilmes. On the Semi-Supervised Learning of Multi-Layered Perceptrons. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brighton, UK, September 2009. Winner of Best First-Author Student Paper Award, INTERSPEECH 2009
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• Jonathan Malkin, Amar Subramanya, and Jeff A. Bilmes. On the Semi-Supervised Learning of Multi-Layered Perceptrons. Technical Report UWEETR-2009-0003, University of Washington, Dept. of EE, 2009.
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2009-0003.html
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• Jon Malkin and Jeff Bilmes. Multi-Layer Ratio Semi-Definite Classifiers. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 2009.
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• Jon Malkin and Jeff Bilmes. Ratio Semi-Definite Classifiers. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Las Vegas, NV, April 2008.
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• Jon Malkin, Brandi House, and Jeff Bilmes. Control of Simulated Arm with the Vocal Joystick. In Striking a Chord: Vocal Interaction in Assistive Technologies, Games, and More: CHI 2007 workshop on non-verbal acoustic interaction, San Jose, CA, April 2007.
  http://www.vocal-input.org
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• Jon Malkin, Neil Lawrence, and Jeff Bilmes. The GP-LVM for Vocal Joystick Control. Technical Report UWEETR-2006-00016, University of Washington, Dept. of EE, 2006. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2006-0016.html
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2006-0016.html
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• Jon Malkin, Xiao Li, and Jeff Bilmes. Energy and Loudness for Speed Control in the Vocal Joystick. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Cancun, Mexico, Nov/Dec 2005.
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• J. Malkin, X. Li, and J. Bilmes. A Graphical Model for Formant Tracking. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Philadelphia, PA, March 2005.
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• Jonathan Malkin, Xiao Li, and Jeff Bilmes. Custom Arithmetic for High-Speed, Low-Resource ASR Systems. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Montreal, Canada, May 2004.
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Mausam

• Mausam, Stephen Soderland, Oren Etzioni, Daniel S. Weld, Michael Skinner, and Jeff A. Bilmes. Compiling a Massive, Multilingual Dictionary via Probabilistic Inference. In Joint Conference of the Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), Suntec, Singapore, August 2009.
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May

• Damon H. May, Jeff Bilmes, and William Stafford Noble. A learned embedding for efficient joint analysis of millions of mass spectra.. bioRxiv, 2018.
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Meila

• Marina Meila, Kapil Phadnis, Arthur Patterson, and Jeff Bilmes. Consensus Ranking Under the Exponential Model. In 22nd Conference on Uncertainty in Artificial Intelligence (UAI07), Vancouver, British Columbia, July 2007.
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Mirzasoleiman

• Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec. Coresets for Data-efficient Training of Machine Learning Models. In International Conference on Machine Learning (ICML), July 2020. http://proceedings.mlr.press/v119/mirzasoleiman20a.html
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Morgan

• N. Morgan, J. Beck, P. Kohn, and J. Bilmes. Neurocomputing on the RAP. In K. W. Przytula and V. K. Prasanna, editors, Digital Parallel Implementations of Neural Networks, pp. 197–219, Prentice-Hall, 1993.
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• N. Morgan, J. Beck, P. Kohn, J. Bilmes, E. Allman, and J. Beer. The Ring Array Processor (RAP): A Multiprocessing peripheral for connectionist applications. Journal of Parallel and Distributed Computing, 14:248–259, April 1992.
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• N. Morgan, J. Beck, P. Kohn, J. Bilmes, E. Allman, and J. Beer. The RAP: a ring array processor for layered network calculations. In Proceedings of the International Conference on Application Specific Array Processors, pp. 296–308, 1990.
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Narang

• Adhyyan Narang, Omid Sadeghi, Lillian J. Ratliff, Maryam Fazel, and Jeff Bilmes. Efficient Interactive Maximization of BP and Weakly Submodular Objectives. In Uncertainty in Artificial Intelligence (UAI), AUAI, Barcelona, Spain, July 2024.
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Narasimhan

• Mukund Narasimhan and Jeff Bilmes. Local Search for Balanced Submodular Clusterings. In Twentieth International Joint Conference on Artificial Intelligence (IJCAI07), Hyderabad, India, January 2007.
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• Mukund Narasimhan and Jeff Bilmes. Learning Graphical Models over partial $k$-trees. Technical Report UWEETR-2006-0001, University of Washington, Department of Electrical Engineering, 2006. https://vannevar.ece.uw.edu/techsite/papers/refer/UWEETR-2006-0001.html
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• Mukund Narasimhan, Nebojsa Jojic, and Jeff Bilmes. Q-Clustering. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2005.
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• Mukund Narasimhan and Jeff Bilmes. A Submodular-Supermodular Procedure with Applicationsto Discriminative Structure Learning. In Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann Publishers, Edinburgh, Scotland, July 2005.
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• Mukund Narasimhan and Jeff Bilmes. PAC-learning bounded tree-width Graphical Models. In Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (UAI-2004), Morgan Kaufmann Publishers, July 2004.
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• Mukund Narasimhan and Jeff Bilmes. Optimal Sub-graphical Models. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2004.
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• Mukund Narasimhan and Jeff Bilmes. Efficient PAC-learning bounded tree-width Graphical Models. Technical Report UWEETR-2004-0009, University of Washington, Dept. of Electrical Engineering, 2004. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2004-0009.html
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• Mukund Narasimhan and Jeff Bilmes. Optimization on Separator Trees. Technical Report UWEETR-2004-0018, University of Washington, Dept. of Electrical Engineering, 2004. https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2004-0018.html
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Niepert

• Mathias Niepert, Pedro Domingos, and Jeff Bilmes. Generalized Conditional Independence and Decomposition Cognizant Curvature: Implications for Function Optimization. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Montreal, Canada, December 2014. NeurIPS (formerly NIPS) Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML)
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P. Singh

• Ajit P. Singh, John Halloran, Jeff A. Bilmes, Katrin Kirchoff, and William S. Noble. Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra. In Uncertainty in Artificial Intelligence (UAI), AUAI, Catalina Island, USA, July 2012.
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Pentney

• William Pentney, Matthai Philipose, and Jeff Bilmes. Structure Learning on Large Scale Common Sense Statistical Models of Human State. In Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08),, Chicago, Illinois, USA, July 2008.
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• William Pentney, Matthai Philipose, Jeff Bilmes, and Henry Kautz. Learning Large Scale Common Sense Models of Everyday Life. In Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07), Vancouver, British Columbia, July 2007.
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Pernkopf

• Franz Pernkopf and Jeff Bilmes. Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers. JMLR: Journal of Machine Learning Research, 11:2323–2360, August 2010.
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• Franz Pernkopf, Van Pham Tuan, and Jeff Bilmes. Broad phonetic classification using discriminative Bayesian networks. Speech Communications, 51(2):151–166, Elsevier Science Publishers B. V., Amsterdam, The Netherlands, The Netherlands, 2009.
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• Franz Pernkopf and Jeff Bilmes. Order-based Discriminative Structure Learning for Bayesian Network Classifiers. In The Tenth International Symposium on Artificial Intelligence and Mathematics (ISAIM 2007), Fort Lauderdale, Florida, 2008.
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• Franz Pernkopf and Jeff Bilmes. Discriminative versus Generative Parameter and Structure Learning of Bayesian Network Classifiers. In International Conference on Machine Learning (ICML), Bonn, Germany, 2005.
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Ping-Chen

• Chia Ping-Chen and Jeff Bilmes. MVA Processing of Speech Features. IEEE Transactions on Audio, Speech, and Language Processing, 15(1), January 2007.
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Powers

• Thomas Powers, Jeff Bilmes, David W. Krout, and Les Atlas. Constrained Robust Submodular Sensor Selection with Applications to Multistatic Sonar Arrays. In 19th International Conference on Information Fusion, IEEE, Heidelberg, Germany, July 2016.
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Prabhakara

• Akarsh Prabhakara, Tao Jin, Arnav Das, Gantavya Bhatt, Lilly Kumari, Elahe Soltanaghai, Jeff Bilmes, Swarun Kumar, and Anthony Rowe. High Resolution Point Clouds from mmWave Radar. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 4135–4142, , 2023.
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• Akarsh Prabhakara, Tao Jin, Arnav Das, Gantavya Bhatt, Lilly Kumari, Elahe Soltanaghai, Jeff Bilmes, Swarun Kumar, and Anthony Rowe. RadarHD: Demonstrating Lidar-like Point Clouds from mmWave Radar. In Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, pp. 1–3, 2023.
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Raj

• Alvin Raj, Amarnag Subramanya, Jeff Bilmes, and Dieter Fox. Rao-Blackwellized particle filters for recognizing activities andspatial context from wearable sensors. In Experimental Robotics: The 10th International Symposium,Springer Tracts in Advanced Robotics (STAR), Springer-Verlag, July 2006.
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Reynolds

• Sheila M. Reynolds, Jeff A. Bilmes, and William Stafford Noble. Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens. PLoS Comput Biol, 6(7):e1000834, Public Library of Science, July 2010.
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• Sheila Reynolds, Jeff Bilmes, and William Noble. Predicting Nucleosome Positioning using Multiple Evidence Tracks. In Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), Lisbon, Portual, April 2010.
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• Sheila Reynolds, Jeff Bilmes, and William Noble. Low Frequency Oscillations in Single-Nucleotide Content Play a Role in Nucleosome Positioning in H. Sapiens. In Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), Tucson, Arizona, April 2009.
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• Sheila Reynolds, Jeff Bilmes, and William Noble. On the relationship between DNA periodicity and local chromatin structure. In Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), Tucson, Arizona, April 2009.
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• Sheila M. Reynolds, Lukas Kall, Michael E. Riffle, Jeff A. Bilmes, and William Noble. Transmembrane topology and signal peptide prediction using dynamic Bayesian networks. PLoS Computational Biology, 4(11), November 2008.
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• S. Reynolds and Jeff Bilmes. Part-of-Speech Tagging using Virtual Evidence and Negative Training. In Human Language Technology Conference/Conference on Empirical Methodsin Natural Language Processing (HLT/EMNLP-2005), Vancouver, B.C., Oct 2005.
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Richardson

• Matt Richardson, Jeff Bilmes, and Chris Diorio. Hidden-articulator Markov models for speech recognition. Speech Communication, 41:511, October 2003.
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• Matt Richardson, Jeff Bilmes, and Chris Diorio. Hidden-Articulator Markov Models: Performance Improvements and Robustness to Noise. In Proc. Int. Conf. on Spoken Language Processing, Beijing, China, October 2000.
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• Matt Richardson, Jeff Bilmes, and Chris Diorio. Hidden-Articulator Markov Models for Speech Recognition. In Proc. of the ISCA ITRW Automatic Speech Recognition 2000 Workshop, Paris, France, October 2000.
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Schmidt

• H. Schmidt and J. Bilmes. Exception Handling in pSather. In Exception Handling Workshop, European Conference on Object-Oriented Programming, 1991.
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Schreiber

• Jacob Schreiber, Jeff Bilmes, and William Stafford Noble. Apricot: Submodular selection for data summarization in Python.. Journal of Machine Learning Research: Software Report, 2020.
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• Jacob Schreiber, Timothy J. Durham, Jeff Bilmes, and William Stafford Noble. Avocado: Multi-scale deep tensor factorization learns a latent representation of the human epigenome. Genome Biology, 21(81), 2020.
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• Jacob Schreiber, Jeff Bilmes, and William Stafford Noble. Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples.. Genome Biology, 22(81), 2020.
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• Jacob Schreiber, Jeff Bilmes, and William Stafford Noble. Prioritizing transcriptomic and epigenomic experiments by using an optimization strategy that leverages imputed data. bioRxiv, 2019.
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• Jacob Schreiber, Ritambhara Singh, Jeff Bilmes, and William Stafford Noble. A pitfall for machine learning methods aiming to predict across cell types. bioRxiv, 2019.
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• Jacob Schreiber, Maxwell Libbrecht, Jeffrey Bilmes, and William Noble. Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture. bioRxiv, Cold Spring Harbor Laboratory, 2017.
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Singh

• Ajit Singh, Andrew Guillory, and Jeff Bilmes. On Bisubmodular Maximization. In Fifteenth International Conference on Artificial Intelligence and Statistics (AISTAT), La Palma, Canary Islands, April 2012.
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Stupakov

• Alex Stupakov, Evan Hanusa, Deepak Vijaywargi, Dieter Fox, and Jeff Bilmes. The design and collection of COSINE, a multi-microphone in situ speech corpus recorded in noisy environments. Computer Speech and Langauge, 26:52–66, 2011.
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• Alex Stupakov, Evan Hanusa, Jeff Bilmes, and Dieter Fox. A Corpus of Multi-Party Conversational Speech in Noisy Environments. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 2009.
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Subramanya

• Amar Subramanya and Jeff Bilmes. Semi-Supervised Learning with Measure Propagation. JMLR: Journal of Machine Learning Research, 12:3311–3370, November 2011.
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• Amar Subramanya and Jeff Bilmes. Semi-Supervised Learning with Measure Propagation. Technical Report UWEETR-2010-0004, University of Washington, Seattle, 2010.
  https://www.ee.washington.edu/techsite/papers/refer/UWEETR-2010-0004.html
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• Amar Subramanya and Jeff A. Bilmes. Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2009.
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• Amar Subramanya and Jeff A. Bilmes. The Semi-Supervised Switchboard Transcription Project. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brighton, UK, September 2009.
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• Amar Subramanya and Jeff Bilmes. Soft-Supervised Learning for Text Classification. In Empirical Methods in Natural Language Processing (EMNLP), Honolulu, Hawaii, October 2008.
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• Amarnag Subramanya and Jeff Bilmes. Applications of Virtual-Evidence based Speech Recognizer Training. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Brisbane, Australia, September 2008.
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• Amar Subramanya, Chris Bartels, Jeff Bilmes, and Patrick Nguyen. Uncertainty in Training Large Vocabulary Speech Recognizers. In Proc. IEEE Automatic Speech Recognition and Understanding (ASRU), Kyoto, Japan, December 2007.
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• Amarnag Subramanya and Jeff Bilmes. Virtual Evidence for Training Speech Recognizers using Partially Labeled Data. In Human Language Technology Conference/North American chapter of the Association for Computational Linguistics (HLT/NAACL-2007), Rochester, NY, April 2007.
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• Amarnag Subramanya, Alvin Raj, Jeff Bilmes, and Dieter Fox. Hierarchical Models for Activity Recognition. In IEEE Multimedia Signal Processing (MMSP) Conference, Victoria, CA, October 2006.
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• Amarnag Subramanya, Alvin Raj, Jeff Bilmes, and Dieter Fox. Recognizing Activities and Spatial Context Using Wearable Sensors. In Uncertainty in Artificial Intelligence (UAI), 21, AUAI, Cambridge, MA, July 2006.
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• Amar Subramanya, Jeff Bilmes, and Chia-Ping Chen. Focused Word Segmentation for ASR. In European Conf. on Speech Communication and Technology (Eurospeech), Lisbon, Portugal, September 2005.
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T.

• L. Atlas T. Powers, J. Bilmes, S. Wisdom, D. Krout. Constrained Robust Submodular Optimization. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Barcelona, Spain, December 2016. NIPS Workshop on Optimization for Machine Learning
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Takiguchi

• Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, and Yasuo Ariki. Evaluation of Random-Projection-Based Feature Combination in Speech Recognition. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Dallas, Texas, 2010.
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Thulasidasan

• Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah Michalak. On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2019.
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• S Thulasidasan, T Bhattacharya, J Bilmes, G Chennupati, and J Mohd-Yusof. Combating Label Noise in Deep Learning Using Abstention. In International Conference on Machine Learning (ICML), 2019.
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• Sunil Thulasidasan and Jeffrey Bilmes. Acoustic classification using Semi-supervised Deep Neural Networks and Stochastic entropy-regularization over Nearest-Neighbor graphs. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, New Orleans, USA, 2017.
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• Sunil Thulasidasan and Jeff Bilmes. Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization. In 2016 Workshop on Machine Learning in Speech and Language Processing, San Francisco, CA, September 2016.
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Tschiatschek

• Sebastian Tschiatschek, Rishabh Iyer, Haochen Wei, and Jeff Bilmes. Learning Mixtures of Submodular Functions for Image Collection Summarization. In Neural Information Processing Society (NeurIPS, formerly NIPS), Montreal, Canada, December 2014.
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Verma

• Sahil Verma, Royi Rassin, Arnav Mohanty Das, Gantavya Bhatt, Preethi Seshadri, Chirag Shah, Jeff Bilmes, Hannaneh Hajishirzi, and Yanai Elazar. How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models. Transactions on Machine Learning Research (TMLR), December 2025. Accepted publication
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• Sahil Verma, Keegan Hines, Jeff Bilmes, Charlotte Siska, Luke Zettlemoyer, Hila Gonen, and Chandan Singh. OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities. In Empirical Methods in Natural Language Processing (EMNLP), November 2025. Note, previous title: MULTIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities EMNLP-2025 Oral Acceptance
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• Sahil Verma, Royi Rassin, Arnav Das, Gantavya Bhatt, Preethi Seshadri, Chirag Shah, Jeff Bilmes, Hannaneh Hajishirzi, and Yanai Elazar. How Many Van Goghs Does It Take to Van Gogh? Finding the Imitation Threshold. Technical Report Arxiv, 2024.
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• Sahil Verma, Gantavya Bhatt, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P Dickerson, and Jeff Bilmes. Effective Backdoor Mitigation Depends on the Pre-training Objective. In NeurIPS 2023 Workshop on Backdoors in Deep Learning-The Good, the Bad, and the Ugly, 2023.
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• Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P Dickerson, and Jeff Bilmes. Effective Backdoor Mitigation Depends on the Pre-training Objective. 2023.
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Vuduc

• Rich Vuduc, Jim Demmel, and Jeff Bilmes. Statistical Models for Empirical Search-Based Performance Tuning. International Journal of High Performance Computing Applications, 18(1):65–94, February 2004.
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• Rich Vuduc, Jim Demmel, and Jeff Bilmes. On Statistical Models in Automatic Tuning. In The International Conference on Computational Science: Architecture-Specific Automatic Performance Tuning, San Francisco, May 2001.
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• Rich Vuduc, Jeff Bilmes, and Jim Demmel. Statistical Modeling of Feedback Data in an Automatic Tuning System. In Third ACM Workshop on Feedback-Directed and Dynamic Optimization FDDO-3, Monterey, December 2000.
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Wang

• Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, and Jeff A Bilmes. Constrained Robust Submodular Partitioning. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2021.
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• Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania, and Jeffrey Bilmes. Fixing Mini-batch Sequences with Hierarchical Robust Partitioning. In 22nd International Conference on Artificial Intelligence and Statistics (AISTATS-2019), April 2019.
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• Shengjie Wang, Haoran Cai, Jeff Bilmes, and William Noble. Training Compressed Fully-Connected Networks with a Density-Diversity Penalty. In Proc. International Conference on Learning Representations, Toulon, France, 2017.
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• Shengjie Wang, John T. Halloran, Jeff A. Bilmes, and William S. Noble. Faster and more accurate graphical model identification of tandem mass spectra using trellises. Bioinformatics, 32(12):i322, 2016.
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• Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff Bilmes, Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, and Ozlem Aslan. Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. In International Conference on Machine Learning (ICML), New York, NY, July 2016.
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• Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. On Deep Multi-View Representation Learning. In International Conference on Machine Learning (ICML), Lille, France, 2015.
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• Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. Unsupervised Learning of Acoustic Features via Deep Canonical Correlation Analysis. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Brisbane Australia, 2015.
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Wei

• Kai Wei, Maxwell W. Libbrecht, Jeffrey A. Bilmes, and William Stafford Noble. Choosing panels of genomics assays using submodular optimization. Genome Biology, 17(1):229, 2016.
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• Kai Wei, Maxwell W Libbrecht, Jeffrey A Bilmes, and William Noble. Choosing panels of genomics assays using submodular optimization (TR). bioRxiv, Cold Spring Harbor Labs Journals, 2016.
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• Kai Wei, Rishabh Iyer, Shengjie Wang, Wenruo Bai, and Jeff Bilmes. How to Intelligently Distribute Training Data to Multiple Compute Nodes: Distributed Machine Learning via Submodular Partitioning. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Montreal, Canada, December 2015. LearningSys Workshop, http://learningsys.org
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• Kai Wei, Rishabh Iyer, Shengjie Wang, Wenruo Bai, and Jeff Bilmes. Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications. In Neural Information Processing Society (NeurIPS, formerly NIPS), Montreal, Canada, December 2015.
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• Kai Wei, Rishabh Iyer, and Jeff Bilmes. Submodularity in Data Subset Selection and Active Learning. In International Conference on Machine Learning (ICML), Lille, France, 2015.
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• Kai Wei, Rishabh Iyer, and Jeff Bilmes. Fast Multi-stage Submodular Maximization. In International Conference on Machine Learning (ICML), Beijing, China, 2014.
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• Kai Wei, Yuzong Liu, Katrin Kirchhoff, Chris Bartels, and Jeff Bilmes. Submodular Subset Selection for Large-Scale Speech Training Data. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Florence, Italy, 2014.
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• Kai Wei, Yuzong Liu, Katrin Kirchhoff, and Jeff Bilmes. Unsupervised Submodular Subset Selection for Speech Data. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Florence, Italy, 2014.
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• Kai Wei, Yuzong Liu, Katrin Kirchhoff, and Jeff Bilmes. Using Document Summarization Techniques for Speech Data Subset Selection. In North American chapter of the Association for Computational Linguistics/Human Language Technology Conference (NAACL/HLT-2013), Atlanta, GA, June 2013.
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Wyatt

• Danny Wyatt, Tanzeem Choudhury, Jeff Bilmes, and James A. Kitts. Inferring Colocation and Conversation Networks from Privacy-Sensitive Audio with Implications for Computational Social Science. ACM Transactions on Intelligent Systems and Technology, 2:7:1–7:41, ACM, New York, NY, USA, January 2010.
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• Danny Wyatt, Tanzeem Choudhury, and Jeff Bilmes. Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models. In Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-10),, Atlanta, GA., July 2010.
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• Danny Wyatt, Tanzeem Choudhury, and Jeff Bilmes. Dynamic Multi-Valued Network Models for Predicting Face-to-Face Conversations. In Neural Information Processing Society (NeurIPS, formerly NIPS) Workshop, Vancouver, Canada, December 2009. NIPS Workshop on Analyzing Networks and Learning with Graphs
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• Danny Wyatt, Tanzeem Choudhury, and Jeff Bilmes. Understanding Social Relationships: Cues from Subtle Conversational Signals. In Tenth International Conference on Ubiquitous Computing (UBICOMP08), Seoul, South Korea, September 2008.
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• Danny Wyatt, Tanzeem Choudhury, and Jeff Bilmes. Learning Hidden Curved Exponential Family Models to Infer Face-to-FaceInteraction Networks from Situated Speech Data. In Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08),, Chicago, Illinois, USA, July 2008.
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• Danny Wyatt, Tanzeem Choudhury, and Jeff Bilmes. Conversation Detection and Speaker Segmentation in Privacy-Sensitive Situated Speech Data. In Proc. Annual Conference of the International Speech Communication Association (INTERSPEECH), Antwerp, Belgium, September 2007.
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• Danny Wyatt, Tanzeem Choudhury, Jeff Bilmes, and Henry Kautz. A Privacy Sensitive Approach to Modeling Multi-Person Conversations. In Twentieth International Joint Conference on Artificial Intelligence (IJCAI07), Hyderabad, India, January 2007.
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Yang

• Rui Yang, Arnav Das, Vianne R. Gao, Alireza Karbalayghareh, William S. Noble, Jeffery A. Bilmes, and Christina S. Leslie. Epiphany: predicting Hi-C contact maps from 1D epigenomic signals. Genome Biology, 24(1):134, 2023.
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• Rui Yang, Arnav Das, Vianne R. Gao, Alirezah Karbalayghareh, William Stafford Noble, Jeff A. Bilmes, and Christina S. Leslie. Epiphany: predicting Hi-C contact maps from 1D epigenomic signals.. bioRxiv, 2022.
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• Wei Yang, Jeff Bilmes, and William Stafford Noble. Submodular sketches of single-cell RNA-seq measurements. In 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), ACM SIGBio, ACM SIGBio, Virtual, September 2020.
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• Wei Yang, Jacob Schreiber, Jeffrey Bilmes, and William Stafford Noble. Submodular sketches of single-cell RNA-seq measurements. bioRxiv, Cold Spring Harbor Laboratory, 2020.
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Yao

• Jihan Yao, Gantavya Bhatt, Arnav Das, Peter Jin, Ke Bao, Qiaolin Yu, Khushi Bhardwaj, Chang Su, Jialei Wang, Yikai Zhu, Sugam Devare, Damon Mosk-Aoyama, Zhen Dong, Venkat Krishna Srinivasan, Yineng Zhang, Oleksii Kuchaiev, Jiantao Jiao, Banghua Zhu, and Jeff Bilmes. Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks. Technical Report Arxiv, 2026.
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Zhang

• Jifan Zhang, Yifang Chen, Gregory Canal, Arnav Mohanty Das, Gantavya Bhatt, Stephen Mussmann, Yinglun Zhu, Jeff Bilmes, Simon Shaolei Du, Kevin Jamieson, and Robert D Nowak. LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning. Journal of Data-centric Machine Learning Research, 2024. Reproducibility Certification
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• Yimin Zhang, Qian Diao, Shan Huang, Wei Hu, Chris Bartels, and Jeff Bilmes. DBN Based Multi-Stream Models for Speech. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Hong Kong, China, May 2003.
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Zhou

• Tianyi Zhou, Shengjie Wang, and Jeff Bilmes. Robust Curriculum Learning: From Clean Label Detection to Noisy Label Self-correction. In International Conference on Learning Representations (ICLR-2021), Virtual Conference, 2021.
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• Tianyi Zhou, Shengjie Wang, and Jeff Bilmes. Curriculum Learning by Optimizing Learning Dynamics. In 24th International Conference on Artificial Intelligence and Statistics (AISTATS-2021), April 2021.
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• Tianyi Zhou, Shengjie Wang, and Jeff Bilmes. Curriculum Learning by Dynamic Instance Hardness. In Neural Information Processing Society (NeurIPS, formerly NIPS), Vancouver, Canada, December 2020.
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• Tianyi Zhou, Shengjie Wang, and Jeff Bilmes. Time-Consistent Self-Supervision for Semi-Supervised Learning. In International Conference on Machine Learning (ICML), July 2020.
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• Tianyi Zhou, Shengjie Wang, and Jeff Bilmes. Diverse Ensemble Evolution: Curriculum based Data-Model Marriage. In Neural Information Processing Society (NeurIPS, formerly NIPS), Montreal, Canada, December 2018.
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• Tianyi Zhou and Jeff Bilmes. Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity. In International Conference on Learning Representations (ICLR-2018), Vancouver, BC, Canada, 2018.
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• Tianyi Zhou, Hua Ouyang, Jeff Bilmes, Yi Chang, and Carlos Guestrin. Scaling Submodular Maximization via Pruned Submodularity Graphs. In 20th International Conference on Artificial Intelligence and Statistics (AISTATS-2017), April 2017.
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• Tianyi Zhou, Jeff Bilmes, and Carlos Guestrin. Divide-and-Conquer Learning by Anchoring a Conical Hull. In Neural Information Processing Society (NeurIPS, formerly NIPS), Montreal, Canada, December 2014.
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Zweig

• Geoff Zweig, Jeff Bilmes, Thomas Richardson, Karim Filali, Karen Livescu, Peng Xu, Kirk Jackson, Yigal Brandman, Eric Sandness, Eva Holtz, Jerry Torres, and Bill Byrne. Structurally Discriminative Graphical Models for Automatic Speech Recognition --- Results from the 2001 Johns Hopkins Summer Workshop. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, 2002.
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