Stochastic First-Order Methods in Data Analysis and Reinforcement Learning
【2017.6.9 10:00am, N202】 |
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2017-06-07
Colloquia & Seminars
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Speaker
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Dr. Mengdi Wang, Princeton University
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Title
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Stochastic First-Order Methods in Data Analysis and Reinforcement Learning
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Time
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2017.6.9 10:00-11:00
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Venue
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N202
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Abstract
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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, and reinforcement learning. We will show that rate of convergence analysis of the stochastic optimization algorithms provide sample complexity analysis for these online learning applications
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Affiliation
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Dr. Mengdi Wang is currently an Assistant Professor in Department of Operations Research and Financial Engineering at Princeton. Dr. Wang received her Ph.D. in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013. Mengdi became an assistant professor at Princeton in 2014. She received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, and the NSF Career Award in 2017.Her research interests are in Data-driven optimization in statistics, machine and reinforcement learning, with applications in healthcare analytics and finance modeling. Her work has been published in journals such as: Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, etc. |
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