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 (ALT, 2011)

No Free Lunch versus Occam's Razor in Supervised Learning. - Tor Lattimore; Marcus Hutter (Algorithmic Probability and Friends, 2011)

PAC Bounds for Discounted MDPs. - Tor Lattimore; Marcus Hutter (ALT, 2012)

Concentration and Confidence for Discrete Bayesian Sequence Predictors. - Tor Lattimore; Marcus Hutter; Peter Sunehag (ALT, 2013)

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

The Sample-Complexity of General Reinforcement Learning. - Tor Lattimore; Marcus Hutter; Peter Sunehag (ICML (3), 2013)

On Martin-Löf Convergence of Solomonoff's Mixture. - Tor Lattimore; Marcus Hutter (TAMC, 2013)

On Learning the Optimal Waiting Time. - Tor Lattimore; András György 001; Csaba Szepesvári (ALT, 2014)

Bayesian Reinforcement Learning with Exploration. - Tor Lattimore; Marcus Hutter (ALT, 2014)

Free Lunch for optimisation under the universal distribution. - Tom Everitt; Tor Lattimore; Marcus Hutter (IEEE Congress on Evolutionary Computation, 2014)

Bounded Regret for Finite-Armed Structured Bandits. - Tor Lattimore; Rémi Munos (NIPS, 2014)

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

Asymptotics of Continuous Bayes for Non-i.i.d. Sources. - Tor Lattimore; Marcus Hutter (CoRR, 2014)

General time consistent discounting. - Tor Lattimore; Marcus Hutter (Theor. Comput. Sci., 2014)

Near-optimal PAC bounds for discounted MDPs. - Tor Lattimore; Marcus Hutter (Theor. Comput. Sci., 2014)

The Pareto Regret Frontier for Bandits. - Tor Lattimore (NIPS, 2015)

Linear Multi-Resource Allocation with Semi-Bandit Feedback. - Tor Lattimore; Koby Crammer; Csaba Szepesvári (NIPS, 2015)

Optimally Confident UCB : Improved Regret for Finite-Armed Bandits. - Tor Lattimore (CoRR, 2015)

On Martin-Löf (non-)convergence of Solomonoff's universal mixture. - Tor Lattimore; Marcus Hutter (Theor. Comput. Sci., 2015)

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

Conservative Bandits. - Yifan Wu; Roshan Shariff; Tor Lattimore; Csaba Szepesvári (ICML, 2016)

On Explore-Then-Commit strategies. - Aurélien Garivier; Tor Lattimore; Emilie Kaufmann (NIPS, 2016)

Refined Lower Bounds for Adversarial Bandits. - Sébastien Gerchinovitz; Tor Lattimore (NIPS, 2016)

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. - Ruitong Huang; Tor Lattimore; András György 001; Csaba Szepesvári (NIPS, 2016)

Causal Bandits: Learning Good Interventions via Causal Inference. - Finnian Lattimore; Tor Lattimore; Mark D. Reid (NIPS, 2016)

Thompson Sampling is Asymptotically Optimal in General Environments. - Jan Leike; Tor Lattimore; Laurent Orseau; Marcus Hutter (UAI, 2016)

Regret Analysis of the Anytime Optimally Confident UCB Algorithm. - Tor Lattimore (CoRR, 2016)

The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. - Tor Lattimore; Csaba Szepesvári (AISTATS, 2017)

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

On Thompson Sampling and Asymptotic Optimality. - Jan Leike; Tor Lattimore; Laurent Orseau; Marcus Hutter (IJCAI, 2017)

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. - Christoph Dann; Tor Lattimore; Emma Brunskill (NIPS, 2017)

A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis. - Tor Lattimore (NIPS, 2017)

UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. - Christoph Dann; Tor Lattimore; Emma Brunskill (CoRR, 2017)

Online Learning with Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; Agnieszka Grabska-Barwinska; Christopher Mattern; Peter Toth (CoRR, 2017)

Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. - Ruitong Huang; Tor Lattimore; András György 001; Csaba Szepesvári (J. Mach. Learn. Res., 2017)

TopRank: A practical algorithm for online stochastic ranking. - Tor Lattimore; Branislav Kveton; Shuai Li 010; Csaba Szepesvári (NeurIPS, 2018)

Single-Agent Policy Tree Search With Guarantees. - Laurent Orseau; Levi Lelis; Tor Lattimore; Theophane Weber (NeurIPS, 2018)

BubbleRank: Safe Online Learning to Rerank. - Branislav Kveton; Chang Li 003; Tor Lattimore; Ilya Markov; Maarten de Rijke; Csaba Szepesvári; Masrour Zoghi (CoRR, 2018)

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Zheng Wen 002; Mohammad Ghavamzadeh; Tor Lattimore (CoRR, 2018)

Refining the Confidence Level for Optimistic Bandit Strategies. - Tor Lattimore (J. Mach. Learn. Res., 2018)

Degenerate Feedback Loops in Recommender Systems. - Ray Jiang; Silvia Chiappa; Tor Lattimore; András György 001; Pushmeet Kohli (AIES, 2019)

Cleaning up the neighborhood: A full classification for adversarial partial monitoring. - Tor Lattimore; Csaba Szepesvári (ALT, 2019)

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

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. - Branislav Kveton; Csaba Szepesvári; Sharan Vaswani; Zheng Wen 002; Tor Lattimore; Mohammad Ghavamzadeh (ICML, 2019)

Online Learning to Rank with Features. - Shuai Li 010; Tor Lattimore; Csaba Szepesvári (ICML, 2019)

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

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

Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. - Julian Zimmert; Tor Lattimore (NeurIPS, 2019)

BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. - Chang Li 003; Branislav Kveton; Tor Lattimore; Ilya Markov; Maarten de Rijke; Csaba Szepesvári; Masrour Zoghi (UAI, 2019)

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

Adaptivity, Variance and Separation for Adversarial Bandits. - Roman Pogodin; Tor Lattimore (CoRR, 2019)

Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees. - Laurent Orseau; Levi H. S. Lelis; Tor Lattimore (CoRR, 2019)

Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; David Budden; Christopher Mattern; Agnieszka Grabska-Barwinska; Peter Toth; Simon Schmitt; Marcus Hutter (CoRR, 2019)

Learning with Good Feature Representations in Bandits and in RL with a Generative Model. - Tor Lattimore; Csaba Szepesvári (CoRR, 2019)

Adaptive Exploration in Linear Contextual Bandit. - Botao Hao; Tor Lattimore; Csaba Szepesvári (AISTATS, 2020)

Information Directed Sampling for Linear Partial Monitoring. - Johannes Kirschner; Tor Lattimore; Andreas Krause 001 (COLT, 2020)

Exploration by Optimisation in Partial Monitoring. - Tor Lattimore; Csaba Szepesvári (COLT, 2020)

Behaviour Suite for Reinforcement Learning. - Ian Osband; Yotam Doron; Matteo Hessel; John Aslanides; Eren Sezener; Andre Saraiva 001; Katrina McKinney; Tor Lattimore; Csaba Szepesvári; Satinder Singh 001; Benjamin Van Roy; Richard S. Sutton; et al. (ICLR, 2020)

Learning with Good Feature Representations in Bandits and in RL with a Generative Model. - Tor Lattimore; Csaba Szepesvári; Gellért Weisz (ICML, 2020)

Linear bandits with Stochastic Delayed Feedback. - Claire Vernade; Alexandra Carpentier; Tor Lattimore; Giovanni Zappella; Beyza Ermis; Michael Brückner (ICML, 2020)

Gaussian Gated Linear Networks. - David Budden; Adam H. Marblestone; Eren Sezener; Tor Lattimore; Gregory Wayne; Joel Veness (NeurIPS, 2020)

High-Dimensional Sparse Linear Bandits. - Botao Hao; Tor Lattimore; Mengdi Wang (NeurIPS, 2020)

Model Selection in Contextual Stochastic Bandit Problems. - Aldo Pacchiano; My Phan; Yasin Abbasi-Yadkori; Anup Rao 002; Julian Zimmert; Tor Lattimore; Csaba Szepesvári (NeurIPS, 2020)

Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation. - Tor Lattimore (CoRR, 2020)

Stochastic matrix games with bandit feedback. - Brendan O'Donoghue; Tor Lattimore; Ian Osband (CoRR, 2020)

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 (AAAI, 2021)

Online Sparse Reinforcement Learning. - Botao Hao; Tor Lattimore; Csaba Szepesvári; Mengdi Wang (AISTATS, 2021)

Asymptotically Optimal Information-Directed Sampling. - Johannes Kirschner; Tor Lattimore; Claire Vernade; Csaba Szepesvári (COLT, 2021)

Improved Regret for Zeroth-Order Stochastic Convex Bandits. - Tor Lattimore; András György 001 (COLT, 2021)

Mirror Descent and the Information Ratio. - Tor Lattimore; András György 001 (COLT, 2021)

Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. - Botao Hao; Yaqi Duan; Tor Lattimore; Csaba Szepesvári; Mengdi Wang (ICML, 2021)

On the Optimality of Batch Policy Optimization Algorithms. - Chenjun Xiao; Yifan Wu; Jincheng Mei; Bo Dai 001; Tor Lattimore; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2021)

Information Directed Sampling for Sparse Linear Bandits. - Botao Hao; Tor Lattimore; Wei Deng 002 (NeurIPS, 2021)

Bandit Phase Retrieval. - Tor Lattimore; Botao Hao (NeurIPS, 2021)

Variational Bayesian Optimistic Sampling. - Brendan O'Donoghue; Tor Lattimore (NeurIPS, 2021)

Matrix games with bandit feedback. - Brendan O'Donoghue; Tor Lattimore; Ian Osband (UAI, 2021)

Geometric Entropic Exploration. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Alaa Saade; Shantanu Thakoor; Bilal Piot; Bernardo Ávila Pires; Michal Valko; Thomas Mesnard; Tor Lattimore; Rémi Munos (CoRR, 2021)

Minimax Regret for Bandit Convex Optimisation of Ridge Functions. - Tor Lattimore (CoRR, 2021)

Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. - Tor Lattimore (COLT, 2022)

Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. - Julian Zimmert; Tor Lattimore (COLT, 2022)

Contextual Information-Directed Sampling. - Botao Hao; Tor Lattimore; Chao Qin (ICML, 2022)

Regret Bounds for Information-Directed Reinforcement Learning. - Botao Hao; Tor Lattimore (NeurIPS, 2022)

A Second-Order Method for Stochastic Bandit Convex Optimisation. - Tor Lattimore; András György 001 (COLT, 2023)

A Lower Bound for Linear and Kernel Regression with Adaptive Covariates. - Tor Lattimore (COLT, 2023)

Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. - Sanae Amani; Tor Lattimore; András György 001; Lin Yang 011 (ICML, 2023)

Leveraging Demonstrations to Improve Online Learning: Quality Matters. - Botao Hao; Rahul Jain 002; Tor Lattimore; Benjamin Van Roy; Zheng Wen 002 (ICML, 2023)

Context-lumpable stochastic bandits. - Chung-Wei Lee; Qinghua Liu; Yasin Abbasi-Yadkori; Chi Jin 001; Tor Lattimore; Csaba Szepesvári (NeurIPS, 2023)

Probabilistic Inference in Reinforcement Learning Done Right. - Jean Tarbouriech; Tor Lattimore; Brendan O'Donoghue (NeurIPS, 2023)

Sequential Best-Arm Identification with Application to Brain-Computer Interface. - Xin Zhou; Botao Hao; Jian Kang; Tor Lattimore; Lexin Li (CoRR, 2023)

Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. - Johannes Kirschner; Tor Lattimore; Andreas Krause 001 (J. Mach. Learn. Res., 2023)

Online Newton Method for Bandit Convex Optimisation Extended Abstract. - Hidde Fokkema; Dirk van der Hoeven; Tor Lattimore; Jack J. Mayo (COLT, 2024)

Bandit Convex Optimisation. - Tor Lattimore (CoRR, 2024)

Online Newton Method for Bandit Convex Optimisation. - Hidde Fokkema; Dirk van der Hoeven; Tor Lattimore; Jack J. Mayo (CoRR, 2024)

Sequential Best-Arm Identification with Application to P300 Speller. - Xin Zhou; Botao Hao; Tor Lattimore; Jian Kang; Lexin Li (Trans. Mach. Learn. Res., 2024)

Thompson Sampling for Bandit Convex Optimisation. - Alireza Bakhtiari; Tor Lattimore; Csaba Szepesvári (COLT, 2025)

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence. - András György 001; Tor Lattimore; Nevena Lazic; Csaba Szepesvári (CoRR, 2025)