Tor Lattimore

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 115

Top Venues

CoRR

NeurIPS

COLT

ICML

Publications

Tor Lattimore
Bandit Convex Optimisation. CoRR (2024)
Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo
Online Newton Method for Bandit Convex Optimisation. CoRR (2024)
Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo
Online Newton Method for Bandit Convex Optimisation Extended Abstract. COLT (2024)
Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen
Leveraging Demonstrations to Improve Online Learning: Quality Matters. CoRR (2023)
Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue
Probabilistic Inference in Reinforcement Learning Done Right. NeurIPS (2023)
Tor Lattimore, András György
A Second-Order Method for Stochastic Bandit Convex Optimisation. COLT (2023)
Johannes Kirschner, Tor Lattimore, Andreas Krause
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications. CoRR (2023)
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
Context-lumpable stochastic bandits. CoRR (2023)
Tor Lattimore
A Lower Bound for Linear and Kernel Regression with Adaptive Covariates. COLT (2023)
Xin Zhou, Botao Hao, Jian Kang, Tor Lattimore, Lexin Li
Sequential Best-Arm Identification with Application to Brain-Computer Interface. CoRR (2023)
Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue
Probabilistic Inference in Reinforcement Learning Done Right. CoRR (2023)
Sanae Amani, Tor Lattimore, András György, Lin Yang
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML (2023)
Tor Lattimore, András György
A Second-Order Method for Stochastic Bandit Convex Optimisation. CoRR (2023)
Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen
Leveraging Demonstrations to Improve Online Learning: Quality Matters. ICML (2023)
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
Context-lumpable stochastic bandits. NeurIPS (2023)
Botao Hao, Tor Lattimore
Regret Bounds for Information-Directed Reinforcement Learning. CoRR (2022)
Sanae Amani, Tor Lattimore, András György, Lin F. Yang
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR (2022)
Botao Hao, Tor Lattimore, Chao Qin
Contextual Information-Directed Sampling. CoRR (2022)
Julian Zimmert, Tor Lattimore
Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. COLT (2022)
Botao Hao, Tor Lattimore, Chao Qin
Contextual Information-Directed Sampling. ICML (2022)
Tor Lattimore
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. CoRR (2022)
Botao Hao, Tor Lattimore
Regret Bounds for Information-Directed Reinforcement Learning. NeurIPS (2022)
Tor Lattimore
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. COLT (2022)
Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter
Gated Linear Networks. AAAI (2021)
Tor Lattimore
Minimax Regret for Bandit Convex Optimisation of Ridge Functions. CoRR (2021)
Tor Lattimore, Botao Hao
Bandit Phase Retrieval. CoRR (2021)
Tor Lattimore, Botao Hao
Bandit Phase Retrieval. NeurIPS (2021)
Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans
On the Optimality of Batch Policy Optimization Algorithms. ICML (2021)
Brendan O'Donoghue, Tor Lattimore
Variational Bayesian Optimistic Sampling. NeurIPS (2021)
Tor Lattimore, András György
Mirror Descent and the Information Ratio. COLT (2021)
Brendan O'Donoghue, Tor Lattimore, Ian Osband
Matrix games with bandit feedback. UAI (2021)
Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
Online Sparse Reinforcement Learning. AISTATS (2021)
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. ICML (2021)
Botao Hao, Tor Lattimore, Wei Deng
Information Directed Sampling for Sparse Linear Bandits. NeurIPS (2021)
Brendan O'Donoghue, Tor Lattimore
Variational Bayesian Optimistic Sampling. CoRR (2021)
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
Geometric Entropic Exploration. CoRR (2021)
Tor Lattimore, András György
Improved Regret for Zeroth-Order Stochastic Convex Bandits. COLT (2021)
Botao Hao, Tor Lattimore, Wei Deng
Information Directed Sampling for Sparse Linear Bandits. CoRR (2021)
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans
On the Optimality of Batch Policy Optimization Algorithms. CoRR (2021)
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
Asymptotically Optimal Information-Directed Sampling. COLT (2021)
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
Asymptotically Optimal Information-Directed Sampling. CoRR (2020)
Tor Lattimore, Csaba Szepesvári
Exploration by Optimisation in Partial Monitoring. COLT (2020)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. ICLR (2020)
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
Model Selection in Contextual Stochastic Bandit Problems. CoRR (2020)
Brendan O'Donoghue, Tor Lattimore, Ian Osband
Stochastic matrix games with bandit feedback. CoRR (2020)
Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner
Linear bandits with Stochastic Delayed Feedback. ICML (2020)
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
Model Selection in Contextual Stochastic Bandit Problems. NeurIPS (2020)
Johannes Kirschner, Tor Lattimore, Andreas Krause
Information Directed Sampling for Linear Partial Monitoring. COLT (2020)
Tor Lattimore, András György
Mirror Descent and the Information Ratio. CoRR (2020)
Tor Lattimore
Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation. CoRR (2020)
Johannes Kirschner, Tor Lattimore, Andreas Krause
Information Directed Sampling for Linear Partial Monitoring. CoRR (2020)
Botao Hao, Tor Lattimore, Mengdi Wang
High-Dimensional Sparse Linear Bandits. CoRR (2020)
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. CoRR (2020)
Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
Online Sparse Reinforcement Learning. CoRR (2020)
David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness
Gaussian Gated Linear Networks. NeurIPS (2020)
David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Greg Wayne, Joel Veness
Gaussian Gated Linear Networks. CoRR (2020)
Botao Hao, Tor Lattimore, Csaba Szepesvári
Adaptive Exploration in Linear Contextual Bandit. AISTATS (2020)
Botao Hao, Tor Lattimore, Mengdi Wang
High-Dimensional Sparse Linear Bandits. NeurIPS (2020)
Tor Lattimore, Csaba Szepesvári, Gellért Weisz
Learning with Good Feature Representations in Bandits and in RL with a Generative Model. ICML (2020)
Tor Lattimore, Csaba Szepesvári
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. CoRR (2019)
Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
Iterative Budgeted Exponential Search. IJCAI (2019)
Laurent Orseau, Tor Lattimore, Shane Legg
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. CoRR (2019)
Roman Pogodin, Tor Lattimore
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits. UAI (2019)
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
Degenerate Feedback Loops in Recommender Systems. AIES (2019)
Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. ICML (2019)
Joel Veness, Tor Lattimore, Avishkar Bhoopchand, David Budden, Christopher Mattern, Agnieszka Grabska-Barwinska, Peter Toth, Simon Schmitt, Marcus Hutter
Gated Linear Networks. CoRR (2019)
Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. UAI (2019)
Tor Lattimore, Csaba Szepesvári
Exploration by Optimisation in Partial Monitoring. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS (2019)
Tor Lattimore, Csaba Szepesvári
Learning with Good Feature Representations in Bandits and in RL with a Generative Model. CoRR (2019)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR (2019)
Shuai Li, Tor Lattimore, Csaba Szepesvári
Online Learning to Rank with Features. ICML (2019)
Julian Zimmert, Tor Lattimore
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. NeurIPS (2019)
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
Degenerate Feedback Loops in Recommender Systems. CoRR (2019)
Roman Pogodin, Tor Lattimore
Adaptivity, Variance and Separation for Adversarial Bandits. CoRR (2019)
Tor Lattimore, Csaba Szepesvári
Cleaning up the neighborhood: A full classification for adversarial partial monitoring. ALT (2019)
Tor Lattimore, Csaba Szepesvári
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. COLT (2019)
Laurent Orseau, Levi H. S. Lelis, Tor Lattimore
Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees. CoRR (2019)
Julian Zimmert, Tor Lattimore
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. CoRR (2019)
Botao Hao, Tor Lattimore, Csaba Szepesvári
Adaptive Exploration in Linear Contextual Bandit. CoRR (2019)
Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
Iterative Budgeted Exponential Search. CoRR (2019)
Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi
BubbleRank: Safe Online Learning to Rerank. CoRR (2018)
Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári
TopRank: A practical algorithm for online stochastic ranking. CoRR (2018)
Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
Single-Agent Policy Tree Search With Guarantees. NeurIPS (2018)
Shuai Li, Tor Lattimore, Csaba Szepesvári
Online Learning to Rank with Features. CoRR (2018)
Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, Théophane Weber
Single-Agent Policy Tree Search With Guarantees. CoRR (2018)
Branislav Kveton, Csaba Szepesvári, Zheng Wen, Mohammad Ghavamzadeh, Tor Lattimore
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. CoRR (2018)
Tor Lattimore
Refining the Confidence Level for Optimistic Bandit Strategies. J. Mach. Learn. Res. 19 (2018)
Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári
TopRank: A practical algorithm for online stochastic ranking. NeurIPS (2018)
Tor Lattimore, Csaba Szepesvári
Cleaning up the neighborhood: A full classification for adversarial partial monitoring. CoRR (2018)
Tor Lattimore, Csaba Szepesvári
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. AISTATS (2017)
Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. J. Mach. Learn. Res. 18 (2017)
Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth
Online Learning with Gated Linear Networks. CoRR (2017)
Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter
On Thompson Sampling and Asymptotic Optimality. IJCAI (2017)
Christoph Dann, Tor Lattimore, Emma Brunskill
UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. CoRR (2017)
Tor Lattimore
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis. NIPS (2017)
Laurent Orseau, Tor Lattimore, Shane Legg
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. ALT (2017)
Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. CoRR (2017)
Christoph Dann, Tor Lattimore, Emma Brunskill
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. NIPS (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Tor Lattimore