Efficient Algorithms for Learning to Control Bandits with Unobserved
Contexts

Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts

2 February 2022

Mohamad Kazem Shirani Faradonbeh

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Papers citing "Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts"

12 / 12 papers shown

Title
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits Yash J. Patel Mohamad Kazem Shirani Faradonbeh 48 15 0 23 Oct 2021
Mirror Descent and the Information Ratio Tor Lattimore András Gyorgy 58 42 0 25 Sep 2020
Bandits with Partially Observable Confounded Data Guy Tennenholtz Uri Shalit Shie Mannor Yonathan Efroni OffRL 48 24 0 11 Jun 2020
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits Manish Raghavan Aleksandrs Slivkins Jennifer Wortman Vaughan Zhiwei Steven Wu 357 18 0 19 May 2020
On Applications of Bootstrap in Continuous Space Reinforcement Learning Mohamad Kazem Shirani Faradonbeh Ambuj Tewari George Michailidis 37 12 0 14 Mar 2019
Input Perturbations for Adaptive Control and Learning Mohamad Kazem Shirani Faradonbeh Ambuj Tewari George Michailidis 66 45 0 10 Nov 2018
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems Mohamad Kazem Shirani Faradonbeh Ambuj Tewari George Michailidis 78 56 0 20 Nov 2017
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach Ouyang Yi Mukul Gagrani A. Nayyar R. Jain 51 128 0 14 Sep 2017
Context Attentive Bandits: Contextual Bandit with Restricted Context Djallel Bouneffouf Irina Rish Guillermo Cecchi Raphael Feraud 42 67 0 10 May 2017
Mostly Exploration-Free Algorithms for Contextual Bandits Hamsa Bastani Mohsen Bayati Khashayar Khosravi 375 159 0 28 Apr 2017
Thompson Sampling for Contextual Bandits with Linear Payoffs Shipra Agrawal Navin Goyal 207 1,006 0 15 Sep 2012
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis E. Kaufmann N. Korda Rémi Munos 179 588 0 18 May 2012