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Events for October 12, 2022
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EE599: Causal Learning course: Casual Bandits
Wed, Oct 12, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ali Tajer, Rensselaer Polytechnic Institute
Talk Title: Causal Bandits
Abstract: In this talk, we provide an overview of the causal bandit problems. The purpose of causal bandit settings is to formalize theoretically-principled frameworks for the experimental design when the experiments involve an array of parameters that causally affect one another. The key objective of causal bandits is to leverage causal relationships to design effective experiments judiciously. Designing causal bandit algorithms critically hinges on the extent of information available about the (i) causal structure and (ii) the interventional distributions. Based on the availability of information on each of these two dimensions, there are, broadly, four possible model combinations. The existing literature, for the most part, focuses on settings in which the interventional distributions are known (with or without knowing the causal structure). First, we provide an overview of the existing literature on the existing literature. Secondly, motivated by the fact that acquiring the interventional distributions is often infeasible, we address the following question: is it possible to achieve the optimal regret scaling rates without knowing the interventional distributions? We address this question affirmatively in the case of linear structural equation models when the causal structure is known. We discuss the design and performance of algorithms for the frequentist and Bayesian settings.
Biography: Ali Tajer received the B.Sc. and M.Sc. degrees in Electrical Engineering from Sharif University of Technology in 2002 and 2004, respectively. During 2007-2010 he was with Columbia University, where he received an M.A degree in Statistics and a Ph.D. degree in Electrical Engineering, and during 2010-2012 he was with Princeton University as a Postdoctoral Research Associate. He is currently an Associate Professor of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute. His research interests include mathematical statistics, statistical signal processing, and network information theory, with applications in wireless communications and power grids. His recent publications include an edited book entitled Advanced Data Analytics for Power Systems (Cambridge University Press, 2021). He received an NSF CAREER award in 2016 and AFRL Faculty Fellowship in 2019. He is currently serving as an Associate Editor for the IEEE Transaction on Information Theory and an Associate Editor for the IEEE Transactions on Signal Processing. In the past, he has served as an Editor for the IEEE Transactions on Communications, a Guest Editor for the IEEE Signal Processing Magazine, an Editor for the IEEE Transactions on Smart Grid, an Editor for the IET Transactions on Smart Grid, and as a Guest Editor-in-Chief for the IEEE Transactions on Smart Grid -“ Special Issue on Theory of Complex Systems with Applications to Smart Grid Operations.
Host: Urbashi Mitra; Password for link: 114454
More Info: https://usc.zoom.us/j/94255391488?pwd=cGoyOVoxWnc3K1RTeVcvYjlWOEJPQT09
Location: Virtual
Audiences: Everyone Is Invited
Contact: Susan Wiedem
Event Link: https://usc.zoom.us/j/94255391488?pwd=cGoyOVoxWnc3K1RTeVcvYjlWOEJPQT09
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Oct 12, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Byron Boots, Paul G. Allen School of Computer Science and Engineering at the University of Washington
Talk Title: Machine Learning for Agile Off-Road Autonomous Driving
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The main goal of this talk is to illustrate how machine learning can start to address some of the fundamental challenges involved in designing intelligent robots. I'll start by discussing off-road driving tasks that require impressive sensing, speed, and agility to complete. I will focus on how machine learning can be combined with prior knowledge and structure to build effective solutions to robotics control problems in this domain. Along the way I'll introduce new tools from reinforcement learning and online learning and show how theoretical insights help us to overcome some of the practical challenges involved in learning on real-world platforms.
Biography: Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Byron's group performs fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception, learning, and control. His work has been applied to a range of problems including localization and mapping, motion planning, robotic manipulation, quadrupedal locomotion, and high-speed navigation. Byron has received several awards including "Best Paper" Awards from ICML, AISTATS, RSS, and IJRR. He is also the recipient of the RSS Early Career Award, the DARPA Young Faculty Award, the NSF CAREER Award, and the Outstanding Junior Faculty Research Award from the College of Computing at Georgia Tech. Byron received his PhD from the Machine Learning Department at Carnegie Mellon University
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/j/98083929768?pwd=SUJreHk0N0ZXbk5QZ1ZPUkRlM3FmZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/98083929768?pwd=SUJreHk0N0ZXbk5QZ1ZPUkRlM3FmZz09
Audiences: Everyone Is Invited
Contact: Talyia White
