Csaba Szepesvari
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Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura
Tadashi Kozuno
Yunhao Tang
Nino Vieillard
Michal Valko
Wenhao Yang
Jincheng Mei
Pierre Menard
Mo Azar
Remi Munos
Olivier Pietquin
Matthieu Geist
Wataru Kumagai
Yutaka Matsuo
International Conference on Machine Learning (ICML) (2023)
Meta-Thompson Sampling
Branislav Kveton
Michael Konobeev
Manzil Zaheer
Martin Mladenov
Proceedings of the 38th International Conference on Machine Learning (ICML 2021), pp. 5884-5893
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao
Yifan Wu
Tor Lattimore
Jincheng Mei
Lihong Li
ICML 2021 (2021)
Differentiable Meta-Learning of Bandit Policies
Branislav Kveton
Martin Mladenov
Manzil Zaheer
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp. 2122-2134
Randomized Exploration in Generalized Linear Bandits
Branislav Kveton
Manzil Zaheer
Lihong Li
Mohammad Ghavamzadeh
23rd International Conference on Artificial Intelligence and Statistics (2020)
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei
Chenjun Xiao
International Conference on Machine Learning (ICML) (2020)
