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Jan 2023: Posted a paper on Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits. Joint work with Ph.D. student Yunlong Hou and former Ph.D. student Zixin Zhong. In this paper, we consider the stochastic combinatorial semi-bandit problem where at each time, the agent's choices at every time instant should contain items whose sum of risks does not exceed a certain risk budget.
Dec 2022: Received 4.8/5.0 for my teaching of some tutorials of the B.Tech. module TG1401 Engineering Mathematics. See review quotes here.
Dec 2022: Paper on establishing the asymptotic Nash equilibrium for the M-ary sequential adversarial hypothesis testing game accepted to the IEEE Transactions on Information Forensics and Security. Joint work with Ph.D. student Jiachun Pan and postdoc Yonglong Li. In this paper, we derive a pair of strategies under which the asymptotic Nash equilibrium of the sequential hypothesis testing game is attained.
Dec 2022: Paper on best arm identification in restless Markov multi-armed bandits accepted to the IEEE Transactions on Information Theory. Joint work with postdocs P. N. Karthik and Srinivas Kota Reddy. In this work, we provide problem instance-dependent asymptotic bounds on the growth rate of the expected time required to find the index of the best arm where the arms evolve according to Markov chains.
Nov 2022: Traveling to NeurIPS 2022. See you in New Orleans!
Nov 2022: Paper on almost optimal variance-constrained best arm identification accepted to the IEEE Transactions on Information Theory. This is joint work with Ph.D. student Yunlong Hou and former student Zixin Zhong. In this paper, we consider the BAI problem under a stringent variance constraint. We propose an algorithm VA-LUCB and show that its performance meets an information-theoretic lower bound.
Nov 2022: Paper on active learning of homogeneous Ising trees accepted to the IEEE Transactions on Information Theory. This is joint work with Ph.D. student Fengzhuo Zhang and former postdoc Anshoo Tandon. In this work, we use active learning techniques to improve the learnability of homogeneous Ising tree models. We show that for high edge correlations, the error exponent can be improved by at least 40% over its passive counterpart.
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