Technical Program

L.12: Multi-Arm Bandits

Session Type: Lecture
Track: Statistics and Learning Theory
Virtual Session: View on Virtual Platform
Session Chair: Christos Thrampoulidis, University of California, Santa Barbara
 
L.12.1: A Hoeffding Inequality For Finite State Markov Chains and its Applications to Markovian Bandits
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         Vrettos Moulos; University of California Berkeley
 
L.12.2: An Improved Regret Bound for Thompson Sampling in the Gaussian Linear Bandit Setting
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         Cem Kalkanli; Stanford University
         Ayfer Ozgur; Stanford University
 
L.12.3: Learned Scheduling of LDPC Decoders Based on Multi-armed Bandits
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         Salman Habib; New Jersey Institute of Tech
         Allison Beemer; New Jersey Institute of Tech
         Joerg Kliewer; New Jersey Institute of Tech
 
L.12.4: Detecting an Odd Restless Markov Arm with a Trembling Hand
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         PN Karthik; Indian Institute of Science
         Rajesh Sundaresan; Indian Institute of Science
 

Plan Ahead

IEEE ISIT 2021

2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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