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<< Wednesday, September 06, 2017 >>


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Stochastic First-Order Methods in Data Analysis and Reinforcement Learning

Seminar: Neyman Seminar | September 6 | 4-5 p.m. | 1011 Evans Hall


Mengdi Wang, Princeton University

Department of Statistics


Stochastic first-order methods provide a basic algorithmic tool for online learning and data analysis. In this talk, we survey several innovative applications including risk-averse optimization, online principal component analysis, dynamic network partition, Markov decision problems and reinforcement learning. We will show that convergence analysis of the stochastic optimization algorithms provide near-optimal sample complexity, run-time complexity and regret analysis in a variety of offline and online learning applications.


(510) 642-2781