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Constrained Maximization of Lattice Submodular Functions
Aytunc Sahin · Joachim Buhmann · Andreas Krause

Sat Jul 18 11:25 AM -- 11:40 AM (PDT) @
in Negative Dependence and Submodularity: Theory and Applications in Machine Learning »

Submodular optimization over the integer lattice has many applications in machine learning. Although the constrained maximization of submodular functions with coordinate-wise concavity (also called {\em DR-Submodular} functions) is well studied, the maximization of {\em general} lattice submodular functions is considerably more challenging. In this work, we first show that we can optimize lattice submodular functions subject to a discrete (integer) polymatroid constraint using a recently proposed extension, called the Generalized Multilinear Extension. Then, we establish a bound on the rounding error for the discrete polymatroid constraint, which depends on the “distance” between the lattice submodular function to a DR-Submodular function. Lastly, we demonstrate the effectiveness of our algorithm on a Bayesian experimental design problem with repetition and a concave cost.

Author Information

Aytunc Sahin (ETH Zurich)
Joachim Buhmann (ETH Zurich)
Andreas Krause (ETH Zurich)
Andreas Krause

Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. He also serves as Academic Co-Director of the Swiss Data Science Center and Chair of the ETH AI Center, and co-founded the ETH spin-off LatticeFlow. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Max Planck Fellow at the Max Planck Institute for Intelligent Systems, an ELLIS Fellow, a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received the Rössler Prize, ERC Starting Investigator and ERC Consolidator grants, the German Pattern Recognition Award, an NSF CAREER award as well as the ETH Golden Owl teaching award. His research has received awards at several premier conferences and journals, including the ACM SIGKDD Test of Time award 2019 and the ICML Test of Time award 2020. Andreas Krause served as Program Co-Chair for ICML 2018, and currently serves as General Chair for ICML 2023 and as Action Editor for the Journal of Machine Learning Research.

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