Date & Time:
April 17, 2019 1:00 pm – 2:00 pm
Location:
Harper Center C04, 5807 S. Woodlawn Ave., Chicago, IL,
04/17/2019 01:00 PM 04/17/2019 02:00 PM America/Chicago Lin Yang (Princeton) – Learn Policy Optimally via Efficiently Utilizing Data University of Chicago and Toyota Technological Institute at Chicago Machine Learning Seminar Series Harper Center C04, 5807 S. Woodlawn Ave., Chicago, IL,

Learn Policy Optimally via Efficiently Utilizing Data

Recent years have witnessed increasing empirical successes in reinforcement learning. Nevertheless, it is an irony that many theoretical problems in this field are not well understood even in the most basic setting. For instance, the optimal sample and time complexities of policy learning in finite-state Markov decision process still remain unclear. Given a state-transition sampler, we develop a novel algorithm that learns an approximate-optimal policy in near-optimal time and using a minimal number of samples. The algorithm makes updates by processing samples in a “streaming” fashion, which requires small memory and naturally adapts to large-scale data. Our result resolves the long-standing open problem on the sample complexity of Markov decision process and provides new insights on how to use data efficiently in learning and optimization.

The algorithm and analysis can be extended to solve two-person stochastic games and feature-based Markov decision problems while achieving near-optimal sample complexity. We further illustrate several other examples of learning and optimization over streaming data, with applications in accelerating Astrophysical discoveries and improving network securities.

Lin Yang

Postdoctoral Researcher, Princeton University

Lin Yang is currently a postdoctoral researcher at Princeton University working with Prof. Mengdi Wang. He obtained two Ph.D. degrees simultaneously in Computer Science and in Physics & Astronomy from Johns Hopkins University in 2017. Prior to that, he obtained a bachelor's degree from Tsinghua University. His research focuses on developing fast algorithms for large-scale optimization and machine learning. This includes reinforcement learning and streaming methods for optimization and function approximations. His algorithms have been applied to real-world applications including accelerating astrophysical discoveries and improving network security. He has published numerous papers in top Computer Science conferences including NeurIPS, ICML, STOC, and PODS. At Johns Hopkins, he was a recipient of the Dean Robert H. Roy Fellowship.

Related News & Events

Students posing at competition
No Name

UChicago Undergrad Team Places Second Overall In Regionals For World’s Largest Programming Competition

Mar 17, 2023
Haifeng Xu
No Name

New CS and DSI Faculty Haifeng Xu Brings Strategic Intelligence to NeurIPS 2022

Nov 28, 2022
No Name

UChicago CS Research Finds New Angle on Database Query Processing with Geometry

Nov 08, 2022
No Name

Asst. Prof. Aloni Cohen Receives Award For Revealing Flaws in Deidentifying Data

Sep 09, 2022
No Name

UChicago Hosts NSF Workshop on Frontiers of Quantum Advantage

Aug 15, 2022
No Name

New 2022-23 Faculty Add Expertise in Linguistics, Visualization, Economics, and Data Science Education

Aug 11, 2022
No Name

UChicago Co-Leads $10 Million NSF Institute on Foundations of Data Science

Aug 09, 2022
No Name

Bill Fefferman Comments on New Standards for Quantum-Proof Cryptography

Jul 07, 2022
No Name

UChicago London Colloquium Features Data Science, Quantum Research

Jul 01, 2022
No Name

Faculty Bill Fefferman and Chenhao Tan Receive Google Research Scholar Awards

Jun 21, 2022
No Name

First-Year PhD Student Co-Authors Outstanding Paper Award Winner at TQC 2022

Apr 28, 2022
No Name

Quanta Magazine Features Prof. Bill Fefferman’s Work on Quantum Algorithms

Jan 20, 2022
arrow-down-largearrow-left-largearrow-right-large-greyarrow-right-large-yellowarrow-right-largearrow-right-smallbutton-arrowclosedocumentfacebookfacet-arrow-down-whitefacet-arrow-downPage 1CheckedCheckedicon-apple-t5backgroundLayer 1icon-google-t5icon-office365-t5icon-outlook-t5backgroundLayer 1icon-outlookcom-t5backgroundLayer 1icon-yahoo-t5backgroundLayer 1internal-yellowinternalintranetlinkedinlinkoutpauseplaypresentationsearch-bluesearchshareslider-arrow-nextslider-arrow-prevtwittervideoyoutube