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Minimax Regret for Bandit Convex Optimisation of Ridge Functions


Jun 06, 2021
Tor Lattimore

* Correcting an (instructive) error that leads to a weaker result 

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Bandit Phase Retrieval


Jun 04, 2021
Tor Lattimore, Botao Hao


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Information Directed Sampling for Sparse Linear Bandits


May 29, 2021
Botao Hao, Tor Lattimore, Wei Deng


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On the Optimality of Batch Policy Optimization Algorithms


Apr 06, 2021
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvari, Dale Schuurmans

* 29 pages, 8 figures 

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Geometric Entropic Exploration


Jan 07, 2021
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Avila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos


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Asymptotically Optimal Information-Directed Sampling


Nov 11, 2020
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári


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High-Dimensional Sparse Linear Bandits


Nov 08, 2020
Botao Hao, Tor Lattimore, Mengdi Wang

* Accepted by NeurIPS 2020 

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Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient


Nov 08, 2020
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang


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Online Sparse Reinforcement Learning


Nov 08, 2020
Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang


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Mirror Descent and the Information Ratio


Sep 25, 2020
Tor Lattimore, András György


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Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation


Jun 19, 2020
Tor Lattimore

* 20 pages, 5 figures. Bound is now improved by d^{1/2} 

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Gaussian Gated Linear Networks


Jun 10, 2020
David Budden, Adam Marblestone, Eren Sezener, Tor Lattimore, Greg Wayne, Joel Veness


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Stochastic matrix games with bandit feedback


Jun 09, 2020
Brendan O'Donoghue, Tor Lattimore, Ian Osband


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Model Selection in Contextual Stochastic Bandit Problems


Mar 03, 2020
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari

* 12 main pages, 2 figures, 14 appendix pages 

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Information Directed Sampling for Linear Partial Monitoring


Feb 25, 2020
Johannes Kirschner, Tor Lattimore, Andreas Krause


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Learning with Good Feature Representations in Bandits and in RL with a Generative Model


Nov 18, 2019
Tor Lattimore, Csaba Szepesvari

* 11 pages 

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Adaptive Exploration in Linear Contextual Bandit


Oct 15, 2019
Botao Hao, Tor Lattimore, Csaba Szepesvari


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Gated Linear Networks


Sep 30, 2019
Joel Veness, Tor Lattimore, Avishkar Bhoopchand, David Budden, Christopher Mattern, Agnieszka Grabska-Barwinska, Peter Toth, Simon Schmitt, Marcus Hutter

* arXiv admin note: substantial text overlap with arXiv:1712.01897 

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Behaviour Suite for Reinforcement Learning


Aug 13, 2019
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt


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Iterative Budgeted Exponential Search


Jul 30, 2019
Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant


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Exploration by Optimisation in Partial Monitoring


Jul 24, 2019
Tor Lattimore, Csaba Szepesvari

* simplified algorithm also works for globally observable, bandit and full information games 

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Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees


Jun 07, 2019
Laurent Orseau, Levi H. S. Lelis, Tor Lattimore

* This paper and another independent IJCAI 2019 submission have been merged into a single paper that subsumes both of them (Helmert et. al., 2019). This paper is placed here only for historical context. Please only cite the subsuming paper 

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Connections Between Mirror Descent, Thompson Sampling and the Information Ratio


May 28, 2019
Julian Zimmert, Tor Lattimore


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Degenerate Feedback Loops in Recommender Systems


Mar 27, 2019
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli

* Proceedings of AAAI/ACM Conference on AI, Ethics, and Society, Honolulu, HI, USA, January 27-28, 2019 (AIES '19) 

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