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  • CS Colloquium: Chi Jin (UC Berkeley) Machine Learning: Why Do Simple Algorithms Work So Well?

    Thu, Mar 07, 2019 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Chi Jin, UC Berkely

    Talk Title: Machine Learning: Why Do Simple Algorithms Work So Well?

    Series: CS Colloquium

    Abstract: While state-of-the-art machine learning models are deep, large-scale, sequential and highly nonconvex, the backbone of modern learning algorithms are simple algorithms such as stochastic gradient descent, or Q-learning (in the case of reinforcement learning tasks). A basic question endures---why do simple algorithms work so well even in these challenging settings?

    This talk focuses on two fundamental problems: (1) in nonconvex optimization, can gradient descent escape saddle points efficiently? (2) in reinforcement learning, is Q-learning sample efficient? We will provide the first line of provably positive answers to both questions. In particular, we will show that simple modifications to these classical algorithms guarantee significantly better properties, which explains the underlying mechanisms behind their favorable performance in practice.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Chi Jin is a Ph.D. candidate in Computer Science at UC Berkeley, advised by Michael I. Jordan. He received a B.S. in Physics from Peking University. His research interests lie in machine learning, statistics, and optimization, with his PhD work primarily focused on nonconvex optimization and reinforcement learning.

    Host: Haipeng Luo

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair


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