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Fall IDS2 Seminar Series

Event Type
Seminar/Symposium
Sponsor
Illinois Institute for Data Science and Dynamical Systems (iDS2) through Coordinated Sciencer Lab Grainger College of Engineering
Virtual
wifi event
Date
Oct 9, 2020   2:00 - 3:00 pm  
Speaker
Semih Cayci, postdoctoral fellow at Illinois Institute for Data Science and Dynamical Systems at University of Illinois at Urbana-Champaign
Registration
Registration
Contact
Peggy Wells
E-Mail
pwells@illinois.edu
Views
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Title: Budget-Constrained Learning and Optimization with Bandit Feedback

Abstract: In numerous decision-making processes such as algorithm portfolio design and adaptive routing, each action incurs a random cost and yields a random and potentially correlated reward, where the process continues until the total cost exceeds a resource constraint. Motivated by these applications, we first investigate the general budgeted bandit problem in which the controller aims to maximize the total reward subject to a budget constraint on the total cost. We show that logarithmic regret is achievable for unbounded cost and reward under mild moment conditions. In particular, we propose robust learning algorithms that incorporate linear estimators to extract and exploit the correlation between the cost and reward of an arm for optimal sample complexity. We prove that these algorithms achieve tight regret bounds, which are optimal up to a constant factor in the Gaussian case. In the second part, we focus on the time-constrained bandit problem, and allow the controller to interrupt an ongoing task and forgo its reward for a potentially faster alternative. We show that this controlled interrupt mechanism improves the total reward linearly over time for a broad class of completion time distributions. We propose learning algorithms that learn the optimal arm and interrupt strategy with order-optimal regret. We show that these learning algorithms provide efficient solutions for boosting the time-efficiency of stochastic local search methods in various important applications that require early stopping, such as the k-SAT problem.

Bio: Semih Cayci is a postdoctoral fellow at Illinois Institute for Data Science and Dynamical Systems at University of Illinois at Urbana-Champaign. He received his PhD in Electrical and Computer Engineering from Ohio State University. His research interests include learning theory, stochastic control and information theory.

NOTE:  Semih Cayci will be available to meet on October 9th. Please email Sanmi Koyejo,(sanmi@illinois.edu) Assistant Professor, Department of Computer Science, University of Illinois at Urbana-Champaign if you would like to be on his schedule. 

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