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SUNMONTUEWEDTHUFRISAT
Events for the 3rd week of February
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Computer Science General Faculty Meeting
Wed, Feb 12, 2020 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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Theory Lunch
Thu, Feb 13, 2020 @ 12:15 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Salil Vadhan, Harvard University
Talk Title: Derandomization Beyond Connectivity: High-Precision Estimation of Random Walks and Laplacian Solvers in Small Space
Abstract: I will describe a series of works that attacks the derandomization of space-bounded computation (e.g. seeking to prove RL=L) using a combination of ideas from the literature on time-efficient Laplacian solvers (Spielman and Teng, STOC '04; Peng and Spielman, STOC '14; Cheng et al. '15; Cohen et al. FOCS '16, STOC '17, FOCS '18) with ones used to show that Undirected S-T Connectivity is in deterministic logspace (Reingold, STOC '05 and JACM '08; Rozenman and Vadhan, RANDOM '05).
In particular, we obtain deterministic, nearly logarithmic-space algorithms for (a) estimating random walk probabilities to within polynomially small error and (b) approximately solving linear systems given by graph Laplacians, with both results holding for Eulerian directed graphs and hence also undirected graphs. Previously both of these problems were known to be solvable for general directed graphs by randomized algorithms in logarithmic space (Aleliunas et al. FOCS '79; Doron, Le Gall, and Ta-Shma RANDOM '17), and hence by deterministic algorithms using space O(log^{3/2} N) (Saks and Zhou, FOCS '95 and JCSS '99).
Joint works with Murtagh, Reingold, and Sidford (FOCS '17 and RANDOM '19) and Ahmadinejad, Kelner, Murtagh, Peebles, and Sidford (arXiv:1912.04524)
Biography: Salil Vadhan is Vicky Joseph Professor of Computer Science and Applied Mathematics at Harvard University. After completing his undergraduate degree in Mathematics and Computer Science at Harvard in 1995, he obtained his PhD in Applied Mathematics from Massachusetts Institute of Technology in 1999, where his advisor was Shafi Goldwasser. His research centers around the interface between computational complexity theory and cryptography. He focuses on the topics of pseudorandomness and zero-knowledge proofs. His work on zig-zag product, with Omer Reingold and Avi Wigderson, was awarded the 2009 Gödel Prize.
Host: Shaddin Dughmi
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Cherie Carter
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CS Colloquium: Scott Niekum (UT Austin) - Scaling Probabilistically Safe Learning to Robotics
Fri, Feb 14, 2020 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Scott Niekum, The University of Texas at Austin
Talk Title: Scaling Probabilistically Safe Learning to Robotics
Series: Computer Science Colloquium
Abstract: Before learning robots can be deployed in the real world, it is critical that probabilistic guarantees can be made about the safety and performance of such systems. In recent years, safe reinforcement learning algorithms have enjoyed success in application areas with high-quality models and plentiful data, but robotics remains a challenging domain for scaling up such approaches. Furthermore, very little work has been done on the even more difficult problem of safe imitation learning, in which the demonstrator's reward function is not known. This talk focuses on new developments in three key areas for scaling safe learning to robotics: (1) a theory of safe imitation learning; (2) scalable reward inference in the absence of models; (3) efficient off-policy policy evaluation. The proposed algorithms offer a blend of safety and practicality, making a significant step towards safe robot learning with modest amounts of real-world data.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Scott Niekum is an Assistant Professor and the director of the Personal Autonomous Robotics Lab (PeARL) in the Department of Computer Science at UT Austin. He is also a core faculty member in the interdepartmental robotics group at UT. Prior to joining UT Austin, Scott was a postdoctoral research fellow at the Carnegie Mellon Robotics Institute and received his Ph.D. from the Department of Computer Science at the University of Massachusetts Amherst. His research interests include imitation learning, reinforcement learning, and robotic manipulation. Scott is a recipient of the 2018 NSF CAREER Award and 2019 AFOSR Young Investigator Award.
Host: Stefanos Nikolaidis
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
