Algorithms & Randomness Center (ARC)
Stefanie Jegelka (MIT)
Monday, September 25, 2017
Caddell Flex Space Rm 122 - 11:00 am
Title: Variations of Submodularity and Diversity: from Robust Optimization to Markov Chains
Abstract:
The combinatorial concept of submodular set functions has proved to be a very useful discrete structure for optimization in machine learning and its applications. In this talk, I will show recent work on generalizations and specializations of this structure, and its connections to robustness and efficiency in machine learning.
First, generalizations to integer and continuous functions lead to algorithms for solving a special class of nonconvex optimization problems. We show how, with further work, this generalization can be leveraged for introducing robustness to uncertainty in budget allocation and bipartite influence maximization problems. The resulting algorithm solves a nonconvex minimax game.
Second, log-submodular discrete probability measures that induce diversity, repulsion and strong notions of negative dependence find applications from randomized matrix approximations and model sketching for large-scale learning to experiment design and interpretable unsupervised learning. But practical sampling methods have hitherto been lagging behind. I will outline how connections to real stable polynomials lead to fast-mixing Markov Chains for practical sampling and to solving an open problem posed by Avron and Boutsidis (2013).
This talk is based on joint work with Matthew Staib, Chengtao Li and Suvrit Sra.
Bio: Stefanie Jegelka is an X-Consortium Career Development Assistant Professor in the Department of EECS at MIT. She is a member of the Computer Science and AI Lab (CSAIL), the Center for Statistics and an affiliate of IDSS and ORC. Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. Stefanie has received an NSF CAREER Award, a DARPA Young Faculty Award, a Google research award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). Her research interests span the theory and practice of algorithmic machine learning.
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