Research focus
About the research
How can algorithms be designed with provable efficiency and robustness for large-scale learning, online decision-making, and control?
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The group researches the design and analysis of algorithms for machine learning and optimization, including adaptive gradient methods like AdaGrad and first sublinear-time algorithms for convex optimization. Researchers develop and analyze projection-free and fast semidefinite programming solvers and online Newton-style methods to obtain logarithmic regret guarantees for online convex optimization. Work also introduces nonstochastic control theory linking optimization and control, with implementations and theoretical analysis to enable provable performance.
Selected publications
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People & alumni
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