Tor Lattimore - RankMe

154 papers A* 40A 8B 11C 2Journal 82Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Diffusion Analysis of Policy Gradient for Stochastic Bandits. CoRR Tor Lattimore
2026	J	`jnl`	A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits. CoRR Tor Lattimore
2026	J	`jnl`	Refined Detection for Gumbel Watermarking. CoRR Tor Lattimore
2025	J	`jnl`	Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence. CoRR András György, Tor Lattimore, Nevena Lazic, Csaba Szepesvári
2025	A*	`conf`	Thompson Sampling for Bandit Convex Optimisation. COLT Alireza Bakhtiari, Tor Lattimore, Csaba Szepesvári
2024	J	`jnl`	Bandit Convex Optimisation. CoRR Tor Lattimore
2024	A*	`conf`	Online Newton Method for Bandit Convex Optimisation Extended Abstract. COLT Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo
2024	J	`jnl`	Online Newton Method for Bandit Convex Optimisation. CoRR Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo
2024	J	`jnl`	Sequential Best-Arm Identification with Application to P300 Speller. Trans. Mach. Learn. Res. Xin Zhou, Botao Hao, Tor Lattimore, Jian Kang, Lexin Li
2023	A*	`conf`	A Lower Bound for Linear and Kernel Regression with Adaptive Covariates. COLT Tor Lattimore
2023	A*	`conf`	A Second-Order Method for Stochastic Bandit Convex Optimisation. COLT Tor Lattimore, András György
2023	J	`jnl`	A Second-Order Method for Stochastic Bandit Convex Optimisation. CoRR Tor Lattimore, András György
2023	A*	`conf`	Context-lumpable stochastic bandits. NeurIPS Chung-wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
2023	J	`jnl`	Context-lumpable stochastic bandits. CoRR Chung-wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
2023	A*	`conf`	Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML Sanae Amani, Tor Lattimore, András György, Lin Yang
2023	A*	`conf`	Leveraging Demonstrations to Improve Online Learning: Quality Matters. ICML Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen
2023	J	`jnl`	Leveraging Demonstrations to Improve Online Learning: Quality Matters. CoRR Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen
2023	J	`jnl`	Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. J. Mach. Learn. Res. Johannes Kirschner, Tor Lattimore, Andreas Krause
2023	J	`jnl`	Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications. CoRR Johannes Kirschner, Tor Lattimore, Andreas Krause
2023	A*	`conf`	Probabilistic Inference in Reinforcement Learning Done Right. NeurIPS Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue
2023	J	`jnl`	Probabilistic Inference in Reinforcement Learning Done Right. CoRR Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue
2023	J	`jnl`	Sequential Best-Arm Identification with Application to Brain-Computer Interface. CoRR Xin Zhou, Botao Hao, Jian Kang, Tor Lattimore, Lexin Li
2022	A*	`conf`	Contextual Information-Directed Sampling. ICML Botao Hao, Tor Lattimore, Chao Qin
2022	J	`jnl`	Contextual Information-Directed Sampling. CoRR Botao Hao, Tor Lattimore, Chao Qin
2022	J	`jnl`	Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR Sanae Amani, Tor Lattimore, András György, Lin F. Yang
2022	A*	`conf`	Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. COLT Tor Lattimore
2022	J	`jnl`	Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. CoRR Tor Lattimore
2022	A*	`conf`	Regret Bounds for Information-Directed Reinforcement Learning. NeurIPS Botao Hao, Tor Lattimore
2022	J	`jnl`	Regret Bounds for Information-Directed Reinforcement Learning. CoRR Botao Hao, Tor Lattimore
2022	A*	`conf`	Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. COLT Julian Zimmert, Tor Lattimore
2021	A*	`conf`	Asymptotically Optimal Information-Directed Sampling. COLT Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
2021	A*	`conf`	Bandit Phase Retrieval. NeurIPS Tor Lattimore, Botao Hao
2021	J	`jnl`	Bandit Phase Retrieval. CoRR Tor Lattimore, Botao Hao
2021	A*	`conf`	Gated Linear Networks. AAAI Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter
2021	J	`jnl`	Geometric Entropic Exploration. CoRR Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos
2021	A*	`conf`	Improved Regret for Zeroth-Order Stochastic Convex Bandits. COLT Tor Lattimore, András György
2021	A*	`conf`	Information Directed Sampling for Sparse Linear Bandits. NeurIPS Botao Hao, Tor Lattimore, Wei Deng
2021	J	`jnl`	Information Directed Sampling for Sparse Linear Bandits. CoRR Botao Hao, Tor Lattimore, Wei Deng
2021	A	`conf`	Matrix games with bandit feedback. UAI Brendan O'Donoghue, Tor Lattimore, Ian Osband
2021	J	`jnl`	Minimax Regret for Bandit Convex Optimisation of Ridge Functions. CoRR Tor Lattimore
2021	A*	`conf`	Mirror Descent and the Information Ratio. COLT Tor Lattimore, András György
2021	A*	`conf`	On the Optimality of Batch Policy Optimization Algorithms. ICML Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2021	J	`jnl`	On the Optimality of Batch Policy Optimization Algorithms. CoRR Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2021	A	`conf`	Online Sparse Reinforcement Learning. AISTATS Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
2021	A*	`conf`	Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. ICML Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
2021	A*	`conf`	Variational Bayesian Optimistic Sampling. NeurIPS Brendan O'Donoghue, Tor Lattimore
2021	J	`jnl`	Variational Bayesian Optimistic Sampling. CoRR Brendan O'Donoghue, Tor Lattimore
2020	A	`conf`	Adaptive Exploration in Linear Contextual Bandit. AISTATS Botao Hao, Tor Lattimore, Csaba Szepesvári
2020	J	`jnl`	Asymptotically Optimal Information-Directed Sampling. CoRR Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
2020	A*	`conf`	Behaviour Suite for Reinforcement Learning. ICLR 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
2020	A*	`conf`	Exploration by Optimisation in Partial Monitoring. COLT Tor Lattimore, Csaba Szepesvári
2020	A*	`conf`	Gaussian Gated Linear Networks. NeurIPS David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness
2020	J	`jnl`	Gaussian Gated Linear Networks. CoRR David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Greg Wayne, Joel Veness
2020	A*	`conf`	High-Dimensional Sparse Linear Bandits. NeurIPS Botao Hao, Tor Lattimore, Mengdi Wang
2020	J	`jnl`	High-Dimensional Sparse Linear Bandits. CoRR Botao Hao, Tor Lattimore, Mengdi Wang
2020	J	`jnl`	Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation. CoRR Tor Lattimore
2020	A*	`conf`	Information Directed Sampling for Linear Partial Monitoring. COLT Johannes Kirschner, Tor Lattimore, Andreas Krause
2020	J	`jnl`	Information Directed Sampling for Linear Partial Monitoring. CoRR Johannes Kirschner, Tor Lattimore, Andreas Krause
2020	A*	`conf`	Learning with Good Feature Representations in Bandits and in RL with a Generative Model. ICML Tor Lattimore, Csaba Szepesvári, Gellért Weisz
2020	A*	`conf`	Linear bandits with Stochastic Delayed Feedback. ICML Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner
2020	J	`jnl`	Mirror Descent and the Information Ratio. CoRR Tor Lattimore, András György
2020	A*	`conf`	Model Selection in Contextual Stochastic Bandit Problems. NeurIPS Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
2020	J	`jnl`	Model Selection in Contextual Stochastic Bandit Problems. CoRR Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
2020	J	`jnl`	Online Sparse Reinforcement Learning. CoRR Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
2020	J	`jnl`	Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. CoRR Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
2020	J	`jnl`	Stochastic matrix games with bandit feedback. CoRR Brendan O'Donoghue, Tor Lattimore, Ian Osband
2019	A*	`conf`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	J	`jnl`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	J	`jnl`	Adaptive Exploration in Linear Contextual Bandit. CoRR Botao Hao, Tor Lattimore, Csaba Szepesvári
2019	J	`jnl`	Adaptivity, Variance and Separation for Adversarial Bandits. CoRR Roman Pogodin, Tor Lattimore
2019	A*	`conf`	An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. COLT Tor Lattimore, Csaba Szepesvári
2019	J	`jnl`	An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. CoRR Tor Lattimore, Csaba Szepesvári
2019	J	`jnl`	Behaviour Suite for Reinforcement Learning. CoRR 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
2019	A	`conf`	BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. UAI Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi
2019	B	`conf`	Cleaning up the neighborhood: A full classification for adversarial partial monitoring. ALT Tor Lattimore, Csaba Szepesvári
2019	A*	`conf`	Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. NeurIPS Julian Zimmert, Tor Lattimore
2019	J	`jnl`	Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. CoRR Julian Zimmert, Tor Lattimore
2019	C	`conf`	Degenerate Feedback Loops in Recommender Systems. AIES Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
2019	J	`jnl`	Degenerate Feedback Loops in Recommender Systems. CoRR Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
2019	J	`jnl`	Exploration by Optimisation in Partial Monitoring. CoRR Tor Lattimore, Csaba Szepesvári
2019	A*	`conf`	Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. ICML Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh
2019	J	`jnl`	Gated Linear Networks. CoRR Joel Veness, Tor Lattimore, Avishkar Bhoopchand, David Budden, Christopher Mattern, Agnieszka Grabska-Barwinska, Peter Toth, Simon Schmitt, Marcus Hutter
2019	A*	`conf`	Iterative Budgeted Exponential Search. IJCAI Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
2019	J	`jnl`	Iterative Budgeted Exponential Search. CoRR Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
2019	J	`jnl`	Learning with Good Feature Representations in Bandits and in RL with a Generative Model. CoRR Tor Lattimore, Csaba Szepesvári
2019	A	`conf`	On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits. UAI Roman Pogodin, Tor Lattimore
2019	A*	`conf`	Online Learning to Rank with Features. ICML Shuai Li, Tor Lattimore, Csaba Szepesvári
2019	J	`jnl`	Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. CoRR Laurent Orseau, Tor Lattimore, Shane Legg
2019	J	`jnl`	Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees. CoRR Laurent Orseau, Levi H. S. Lelis, Tor Lattimore
2018	J	`jnl`	BubbleRank: Safe Online Learning to Rerank. CoRR Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi
2018	J	`jnl`	Cleaning up the neighborhood: A full classification for adversarial partial monitoring. CoRR Tor Lattimore, Csaba Szepesvári
2018	J	`jnl`	Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. CoRR Branislav Kveton, Csaba Szepesvári, Zheng Wen, Mohammad Ghavamzadeh, Tor Lattimore
2018	J	`jnl`	Online Learning to Rank with Features. CoRR Shuai Li, Tor Lattimore, Csaba Szepesvári
2018	J	`jnl`	Refining the Confidence Level for Optimistic Bandit Strategies. J. Mach. Learn. Res. Tor Lattimore
2018	A*	`conf`	Single-Agent Policy Tree Search With Guarantees. NeurIPS Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
2018	J	`jnl`	Single-Agent Policy Tree Search With Guarantees. CoRR Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, Théophane Weber
2018	A*	`conf`	TopRank: A practical algorithm for online stochastic ranking. NeurIPS Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári
2018	J	`jnl`	TopRank: A practical algorithm for online stochastic ranking. CoRR Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári
2017	—	`conf`	A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis. NIPS Tor Lattimore
2017	J	`jnl`	Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. CoRR Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
2017	J	`jnl`	Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. J. Mach. Learn. Res. Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
2017	A*	`conf`	On Thompson Sampling and Asymptotic Optimality. IJCAI Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter
2017	J	`jnl`	Online Learning with Gated Linear Networks. CoRR Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth
2017	B	`conf`	Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. ALT Laurent Orseau, Tor Lattimore, Shane Legg
2017	A	`conf`	The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. AISTATS Tor Lattimore, Csaba Szepesvári
2017	J	`jnl`	UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. CoRR Christoph Dann, Tor Lattimore, Emma Brunskill
2017	—	`conf`	Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. NIPS Christoph Dann, Tor Lattimore, Emma Brunskill
2016	—	`conf`	Causal Bandits: Learning Good Interventions via Causal Inference. NIPS Finnian Lattimore, Tor Lattimore, Mark D. Reid
2016	J	`jnl`	Causal Bandits: Learning Good Interventions via Causal Inference. CoRR Finnian Lattimore, Tor Lattimore, Mark D. Reid
2016	A*	`conf`	Conservative Bandits. ICML Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári
2016	J	`jnl`	Conservative Bandits. CoRR Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári
2016	—	`conf`	Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. NIPS Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
2016	J	`jnl`	Free Lunch for Optimisation under the Universal Distribution. CoRR Tom Everitt, Tor Lattimore, Marcus Hutter
2016	J	`jnl`	On Explore-Then-Commit Strategies. CoRR Aurélien Garivier, Emilie Kaufmann, Tor Lattimore
2016	—	`conf`	On Explore-Then-Commit strategies. NIPS Aurélien Garivier, Tor Lattimore, Emilie Kaufmann
2016	—	`conf`	Refined Lower Bounds for Adversarial Bandits. NIPS Sébastien Gerchinovitz, Tor Lattimore
2016	J	`jnl`	Refined Lower Bounds for Adversarial Bandits. CoRR Sébastien Gerchinovitz, Tor Lattimore
2016	J	`jnl`	Regret Analysis of the Anytime Optimally Confident UCB Algorithm. CoRR Tor Lattimore
2016	A*	`conf`	Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits. COLT Tor Lattimore
2016	J	`jnl`	The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. CoRR Tor Lattimore, Csaba Szepesvári
2016	A	`conf`	Thompson Sampling is Asymptotically Optimal in General Environments. UAI Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter
2016	J	`jnl`	Thompson Sampling is Asymptotically Optimal in General Environments. CoRR Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter
2015	—	`conf`	Linear Multi-Resource Allocation with Semi-Bandit Feedback. NIPS Tor Lattimore, Koby Crammer, Csaba Szepesvári
2015	J	`jnl`	On Martin-Löf (non-)convergence of Solomonoff's universal mixture. Theor. Comput. Sci. Tor Lattimore, Marcus Hutter
2015	J	`jnl`	Optimally Confident UCB : Improved Regret for Finite-Armed Bandits. CoRR Tor Lattimore
2015	J	`jnl`	Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits. CoRR Tor Lattimore
2015	—	`conf`	The Pareto Regret Frontier for Bandits. NIPS Tor Lattimore
2015	J	`jnl`	The Pareto Regret Frontier for Bandits. CoRR Tor Lattimore
2014	J	`jnl`	Asymptotics of Continuous Bayes for Non-i.i.d. Sources. CoRR Tor Lattimore, Marcus Hutter
2014	B	`conf`	Bayesian Reinforcement Learning with Exploration. ALT Tor Lattimore, Marcus Hutter
2014	—	`conf`	Bounded Regret for Finite-Armed Structured Bandits. NIPS Tor Lattimore, Rémi Munos
2014	J	`jnl`	Bounded Regret for Finite-Armed Structured Bandits. CoRR Tor Lattimore, Rémi Munos
2014	B	`conf`	Free Lunch for optimisation under the universal distribution. IEEE Congress on Evolutionary Computation Tom Everitt, Tor Lattimore, Marcus Hutter
2014	J	`jnl`	General time consistent discounting. Theor. Comput. Sci. Tor Lattimore, Marcus Hutter
2014	J	`jnl`	Near-optimal PAC bounds for discounted MDPs. Theor. Comput. Sci. Tor Lattimore, Marcus Hutter
2014	B	`conf`	On Learning the Optimal Waiting Time. ALT Tor Lattimore, András György, Csaba Szepesvári
2014	A	`conf`	Optimal Resource Allocation with Semi-Bandit Feedback. UAI Tor Lattimore, Koby Crammer, Csaba Szepesvári
2014	J	`jnl`	Optimal Resource Allocation with Semi-Bandit Feedback. CoRR Tor Lattimore, Koby Crammer, Csaba Szepesvári
2013	B	`conf`	Concentration and Confidence for Discrete Bayesian Sequence Predictors. ALT Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	J	`jnl`	Concentration and Confidence for Discrete Bayesian Sequence Predictors. CoRR Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	C	`conf`	On Martin-Löf Convergence of Solomonoff's Mixture. TAMC Tor Lattimore, Marcus Hutter
2013	—	`conf`	The Sample-Complexity of General Reinforcement Learning. ICML (3) Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	J	`jnl`	The Sample-Complexity of General Reinforcement Learning. CoRR Tor Lattimore, Marcus Hutter, Peter Sunehag
2013	B	`conf`	Universal Knowledge-Seeking Agents for Stochastic Environments. ALT Laurent Orseau, Tor Lattimore, Marcus Hutter
2012	J	`jnl`	PAC Bounds for Discounted MDPs CoRR Tor Lattimore, Marcus Hutter
2012	B	`conf`	PAC Bounds for Discounted MDPs. ALT Tor Lattimore, Marcus Hutter
2011	J	`jnl`	Asymptotically Optimal Agents CoRR Tor Lattimore, Marcus Hutter
2011	B	`conf`	Asymptotically Optimal Agents. ALT Tor Lattimore, Marcus Hutter
2011	J	`jnl`	No Free Lunch versus Occam's Razor in Supervised Learning CoRR Tor Lattimore, Marcus Hutter
2011	—	`conf`	No Free Lunch versus Occam's Razor in Supervised Learning. Algorithmic Probability and Friends Tor Lattimore, Marcus Hutter
2011	J	`jnl`	Time Consistent Discounting CoRR Tor Lattimore, Marcus Hutter
2011	B	`conf`	Time Consistent Discounting. ALT Tor Lattimore, Marcus Hutter
2011	J	`jnl`	Universal Prediction of Selected Bits CoRR Tor Lattimore, Marcus Hutter, Vaibhav Gavane
2011	B	`conf`	Universal Prediction of Selected Bits. ALT Tor Lattimore, Marcus Hutter, Vaibhav Gavane