Lattimore, Tor

72 publications

COLT 2025 Thompson Sampling for Bandit Convex Optimisation Alireza Bakhtiari, Tor Lattimore, Csaba Szepesvári

COLT 2024 Online Newton Method for Bandit Convex Optimisation Extended Abstract Hidde Fokkema, Dirk Hoeven, Tor Lattimore, Jack J. Mayo

TMLR 2024 Sequential Best-Arm Identification with Application to P300 Speller Xin Zhou, Botao Hao, Tor Lattimore, Jian Kang, Lexin Li

COLT 2023 A Lower Bound for Linear and Kernel Regression with Adaptive Covariates Tor Lattimore

COLT 2023 A Second-Order Method for Stochastic Bandit Convex Optimisation Tor Lattimore, András György

NeurIPS 2023 Context-Lumpable Stochastic Bandits Chung-Wei Lee, Qinghua Liu, Yasin Abbasi Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvari

ICML 2023 Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost Sanae Amani, Tor Lattimore, András György, Lin Yang

ICML 2023 Leveraging Demonstrations to Improve Online Learning: Quality Matters Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen

JMLR 2023 Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications Johannes Kirschner, Tor Lattimore, Andreas Krause

NeurIPS 2023 Probabilistic Inference in Reinforcement Learning Done Right Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue

ICML 2022 Contextual Information-Directed Sampling Botao Hao, Tor Lattimore, Chao Qin

COLT 2022 Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini’s Regret Tor Lattimore

NeurIPS 2022 Regret Bounds for Information-Directed Reinforcement Learning Botao Hao, Tor Lattimore

COLT 2022 Return of the Bias: Almost Minimax Optimal High Probability Bounds for Adversarial Linear Bandits Julian Zimmert, Tor Lattimore

AISTATS 2021 Online Sparse Reinforcement Learning Botao Hao, Tor Lattimore, Csaba Szepesvari, Mengdi Wang

COLT 2021 Asymptotically Optimal Information-Directed Sampling Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvari

NeurIPS 2021 Bandit Phase Retrieval Tor Lattimore, Botao Hao

AAAI 2021 Gated Linear Networks Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter

COLT 2021 Improved Regret for Zeroth-Order Stochastic Convex Bandits Tor Lattimore, Andras Gyorgy

NeurIPS 2021 Information Directed Sampling for Sparse Linear Bandits Botao Hao, Tor Lattimore, Wei Deng

UAI 2021 Matrix Games with Bandit Feedback Brendan O’Donoghue, Tor Lattimore, Ian Osband

COLT 2021 Mirror Descent and the Information Ratio Tor Lattimore, Andras Gyorgy

ICML 2021 On the Optimality of Batch Policy Optimization Algorithms Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvari, Dale Schuurmans

ICML 2021 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvari, Mengdi Wang

NeurIPS 2021 Variational Bayesian Optimistic Sampling Brendan O'Donoghue, Tor Lattimore

AISTATS 2020 Adaptive Exploration in Linear Contextual Bandit Botao Hao, Tor Lattimore, Csaba Szepesvari

ICLR 2020 Behaviour Suite for Reinforcement Learning Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt

COLT 2020 Exploration by Optimisation in Partial Monitoring Tor Lattimore, Csaba Szepesvári

NeurIPS 2020 Gaussian Gated Linear Networks David Budden, Adam Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness

NeurIPS 2020 High-Dimensional Sparse Linear Bandits Botao Hao, Tor Lattimore, Mengdi Wang

COLT 2020 Information Directed Sampling for Linear Partial Monitoring Johannes Kirschner, Tor Lattimore, Andreas Krause

ICML 2020 Learning with Good Feature Representations in Bandits and in RL with a Generative Model Tor Lattimore, Csaba Szepesvari, Gellert Weisz

ICML 2020 Linear Bandits with Stochastic Delayed Feedback Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner

NeurIPS 2020 Model Selection in Contextual Stochastic Bandit Problems Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari

NeurIPS 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

COLT 2019 An Information-Theoretic Approach to Minimax Regret in Partial Monitoring Tor Lattimore, Csaba Szepesvári

UAI 2019 BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi

ALT 2019 Cleaning up the Neighborhood: A Full Classification for Adversarial Partial Monitoring Tor Lattimore, Csaba Szepesvári

NeurIPS 2019 Connections Between Mirror Descent, Thompson Sampling and the Information Ratio Julian Zimmert, Tor Lattimore

ICML 2019 Garbage in, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh

IJCAI 2019 Iterative Budgeted Exponential Search Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant

UAI 2019 On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits Roman Pogodin, Tor Lattimore

ICML 2019 Online Learning to Rank with Features Shuai Li, Tor Lattimore, Csaba Szepesvari

JMLR 2018 Refining the Confidence Level for Optimistic Bandit Strategies Tor Lattimore

NeurIPS 2018 Single-Agent Policy Tree Search with Guarantees Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber

NeurIPS 2018 TopRank: A Practical Algorithm for Online Stochastic Ranking Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvari

NeurIPS 2017 A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis Tor Lattimore

JMLR 2017 Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári

IJCAI 2017 On Thompson Sampling and Asymptotic Optimality Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter

ALT 2017 Soft-Bayes: Prod for Mixtures of Experts with Log-Loss Laurent Orseau, Tor Lattimore, Shane Legg

AISTATS 2017 The End of Optimism? an Asymptotic Analysis of Finite-Armed Linear Bandits Tor Lattimore, Csaba Szepesvári

NeurIPS 2017 Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann, Tor Lattimore, Emma Brunskill

NeurIPS 2016 Causal Bandits: Learning Good Interventions via Causal Inference Finnian Lattimore, Tor Lattimore, Mark D. Reid

ICML 2016 Conservative Bandits Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari

NeurIPS 2016 Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvari

NeurIPS 2016 On Explore-Then-Commit Strategies Aurelien Garivier, Tor Lattimore, Emilie Kaufmann

NeurIPS 2016 Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz, Tor Lattimore

COLT 2016 Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits Tor Lattimore

UAI 2016 Thompson Sampling Is Asymptotically Optimal in General Environments Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter

NeurIPS 2015 Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvari

NeurIPS 2015 The Pareto Regret Frontier for Bandits Tor Lattimore

ALT 2014 Bayesian Reinforcement Learning with Exploration Tor Lattimore, Marcus Hutter

NeurIPS 2014 Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore, Remi Munos

ALT 2014 On Learning the Optimal Waiting Time Tor Lattimore, András György, Csaba Szepesvári

UAI 2014 Optimal Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvári

ALT 2013 Concentration and Confidence for Discrete Bayesian Sequence Predictors Tor Lattimore, Marcus Hutter, Peter Sunehag

ICML 2013 The Sample-Complexity of General Reinforcement Learning Tor Lattimore, Marcus Hutter, Peter Sunehag

ALT 2013 Universal Knowledge-Seeking Agents for Stochastic Environments Laurent Orseau, Tor Lattimore, Marcus Hutter

ALT 2012 PAC Bounds for Discounted MDPs Tor Lattimore, Marcus Hutter

ALT 2011 Asymptotically Optimal Agents Tor Lattimore, Marcus Hutter

ALT 2011 Time Consistent Discounting Tor Lattimore, Marcus Hutter

ALT 2011 Universal Prediction of Selected Bits Tor Lattimore, Marcus Hutter, Vaibhav Gavane