@COMMENT This bibtex publication entry came from Jeff Bilmes's publication pages at
@COMMENT http://melodi.ee.washington.edu/~bilmes
@COMMENT The complete bibfile can be found at:
@COMMENT http://melodi.ee.washington.edu/~bilmes/bib/bilmes.bib
@COMMENT -
@InProceedings{narang2024uai-online-sub-sup,
   Author = {Adhyyan Narang and Omid Sadeghi and Lillian J. Ratliff and Maryam Fazel and Jeff Bilmes},
   Title = {Efficient Interactive Maximization of BP and Weakly Submodular Objectives},
   booktitle =  {Uncertainty in Artificial Intelligence (UAI)},
   year = 	 {2024},
   address = 	 {Barcelona, Spain},
   month = 	 {July},
   publisher = {AUAI},
   abstract = {In the context of online interactive machine learning with combinatorial objectives, we extend purely submodular prior work to more general non-submodular objectives. This includes: (1) those that are additively decomposable into a sum of two terms (a monotone submodular and monotone supermodular term, known as a BP decomposition); and (2) those that are only weakly submodular. In both cases, this allows representing not only competitive (submodular) but also complementary (supermodular) relationships between objects, enhancing this setting to a broader range of applications (e.g., movie recommendations, medical treatments, etc.) where this is beneficial. In the two-term case, moreover, we study not only the more typical monolithic feedback approach but also a novel framework where feedback is available separately for each term. With real-world practicality and scalability in mind, we integrate \Nystrom{} sketching techniques to significantly improve the computational complexity, including for the purely submodular case. In the Gaussian process contextual bandits setting, we show sub-linear theoretical regret bounds in all cases. We also empirically show good applicability to recommendation systems and data subset selection.},
}