Tightness of LP Relaxations for Almost Balanced Models. - Adrian Weller; Mark Rowland 001; David A. Sontag (AISTATS, 2016)

Black-Box Alpha Divergence Minimization. - José Miguel Hernández-Lobato; Yingzhen Li; Mark Rowland 001; Thang D. Bui; Daniel Hernández-Lobato; Richard E. Turner (ICML, 2016)

Conditions beyond treewidth for tightness of higher-order LP relaxations. - Mark Rowland 001; Aldo Pacchiano; Adrian Weller (AISTATS, 2017)

Magnetic Hamiltonian Monte Carlo. - Nilesh Tripuraneni; Mark Rowland 001; Zoubin Ghahramani; Richard E. Turner (ICML, 2017)

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. - Krzysztof Marcin Choromanski; Mark Rowland 001; Adrian Weller (NIPS, 2017)

Uprooting and Rerooting Higher-Order Graphical Models. - Mark Rowland 001; Adrian Weller (NIPS, 2017)

Distributional Reinforcement Learning With Quantile Regression. - Will Dabney; Mark Rowland 001; Marc G. Bellemare; Rémi Munos (AAAI, 2018)

The Geometry of Random Features. - Krzysztof Choromanski; Mark Rowland 001; Tamás Sarlós; Vikas Sindhwani; Richard E. Turner; Adrian Weller (AISTATS, 2018)

An Analysis of Categorical Distributional Reinforcement Learning. - Mark Rowland 001; Marc G. Bellemare; Will Dabney; Rémi Munos; Yee Whye Teh (AISTATS, 2018)

Gaussian Process Behaviour in Wide Deep Neural Networks. - Alexander G. de G. Matthews; Jiri Hron; Mark Rowland 001; Richard E. Turner; Zoubin Ghahramani (ICLR (Poster), 2018)

Structured Evolution with Compact Architectures for Scalable Policy Optimization. - Krzysztof Choromanski; Mark Rowland 001; Vikas Sindhwani; Richard E. Turner; Adrian Weller (ICML, 2018)

Geometrically Coupled Monte Carlo Sampling. - Mark Rowland 001; Krzysztof Choromanski; François Chalus; Aldo Pacchiano; Tamás Sarlós; Richard E. Turner; Adrian Weller (NeurIPS, 2018)

Orthogonal Estimation of Wasserstein Distances. - Mark Rowland 001; Jiri Hron; Yunhao Tang; Krzysztof Choromanski; Tamás Sarlós; Adrian Weller (AISTATS, 2019)

Unifying Orthogonal Monte Carlo Methods. - Krzysztof Choromanski; Mark Rowland 001; Wenyu Chen 003; Adrian Weller (ICML, 2019)

Statistics and Samples in Distributional Reinforcement Learning. - Mark Rowland 001; Robert Dadashi; Saurabh Kumar 004; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2019)

Multiagent Evaluation under Incomplete Information. - Mark Rowland 001; Shayegan Omidshafiei; Karl Tuyls; Julien Pérolat; Michal Valko; Georgios Piliouras; Rémi Munos (NeurIPS, 2019)

α-Rank: Multi-Agent Evaluation by Evolution. - Shayegan Omidshafiei; Christos H. Papadimitriou; Georgios Piliouras; Karl Tuyls; Mark Rowland 001; Jean-Baptiste Lespiau; Wojciech M. Czarnecki; Marc Lanctot; Julien Pérolat; Rémi Munos (CoRR, 2019)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Antithetic and Monte Carlo kernel estimators for partial rankings. - Maria Lomeli; Mark Rowland 001; Arthur Gretton; Zoubin Ghahramani (Stat. Comput., 2019)

Adaptive Trade-Offs in Off-Policy Learning. - Mark Rowland 001; Will Dabney; Rémi Munos (AISTATS, 2020)

Conditional Importance Sampling for Off-Policy Learning. - Mark Rowland 001; Anna Harutyunyan; Hado van Hasselt; Diana Borsa; Tom Schaul; Rémi Munos; Will Dabney (AISTATS, 2020)

A Generalized Training Approach for Multiagent Learning. - Paul Muller; Shayegan Omidshafiei; Mark Rowland 001; Karl Tuyls; Julien Pérolat; Siqi Liu 002; Daniel Hennes; Luke Marris; Marc Lanctot; Edward Hughes 001; Zhe Wang 055; Guy Lever; et al. (ICLR, 2020)

Revisiting Fundamentals of Experience Replay. - William Fedus; Prajit Ramachandran; Rishabh Agarwal; Yoshua Bengio; Hugo Larochelle; Mark Rowland 001; Will Dabney (ICML, 2020)

Fast computation of Nash Equilibria in Imperfect Information Games. - Rémi Munos; Julien Pérolat; Jean-Baptiste Lespiau; Mark Rowland 001; Bart De Vylder; Marc Lanctot; Finbarr Timbers; Daniel Hennes; Shayegan Omidshafiei; Audrunas Gruslys; Mohammad Gheshlaghi Azar; Edward Lockhart; et al. (ICML, 2020)

Navigating the Landscape of Games. - Shayegan Omidshafiei; Karl Tuyls; Wojciech M. Czarnecki; Francisco C. Santos; Mark Rowland 001; Jerome T. Connor; Daniel Hennes; Paul Muller; Julien Pérolat; Bart De Vylder; Audrunas Gruslys; Rémi Munos (CoRR, 2020)

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. - Will Dabney; André Barreto 001; Mark Rowland 001; Robert Dadashi; John Quan; Marc G. Bellemare; David Silver (AAAI, 2021)

On the Effect of Auxiliary Tasks on Representation Dynamics. - Clare Lyle; Mark Rowland 001; Georg Ostrovski; Will Dabney (AISTATS, 2021)

Revisiting Peng's Q(λ) for Modern Reinforcement Learning. - Tadashi Kozuno; Yunhao Tang; Mark Rowland 001; Rémi Munos; Steven Kapturowski; Will Dabney; Michal Valko; David Abel (ICML, 2021)

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. - Julien Pérolat; Rémi Munos; Jean-Baptiste Lespiau; Shayegan Omidshafiei; Mark Rowland 001; Pedro A. Ortega; Neil Burch; Thomas W. Anthony 001; David Balduzzi; Bart De Vylder; Georgios Piliouras; Marc Lanctot; et al. (ICML, 2021)

Taylor Expansion of Discount Factors. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (ICML, 2021)

MICo: Improved representations via sampling-based state similarity for Markov decision processes. - Pablo Samuel Castro; Tyler Kastner; Prakash Panangaden; Mark Rowland 001 (NeurIPS, 2021)

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Rémi Munos; Michal Valko (NeurIPS, 2021)

MICo: Learning improved representations via sampling-based state similarity for Markov decision processes. - Pablo Samuel Castro; Tyler Kastner; Prakash Panangaden; Mark Rowland 001 (CoRR, 2021)

Evolutionary Dynamics and Φ-Regret Minimization in Games. - Georgios Piliouras; Mark Rowland 001; Shayegan Omidshafiei; Romuald Elie; Daniel Hennes; Jerome T. Connor; Karl Tuyls (CoRR, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

Marginalized Operators for Off-policy Reinforcement Learning. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (AISTATS, 2022)

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. - Paul Muller; Mark Rowland 001; Romuald Elie; Georgios Piliouras; Julien Pérolat; Mathieu Laurière; Raphaël Marinier; Olivier Pietquin; Karl Tuyls (AAMAS, 2022)

Understanding and Preventing Capacity Loss in Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney (ICLR, 2022)

Learning Dynamics and Generalization in Deep Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney; Marta Kwiatkowska; Yarin Gal (ICML, 2022)

Generalised Policy Improvement with Geometric Policy Composition. - Shantanu Thakoor; Mark Rowland 001; Diana Borsa; Will Dabney; Rémi Munos; André Barreto 001 (ICML, 2022)

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Bernardo Ávila Pires; Will Dabney; Marc G. Bellemare (NeurIPS, 2022)

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Mark Rowland 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

Learning Dynamics and Generalization in Reinforcement Learning. - Clare Lyle; Mark Rowland 001; Will Dabney; Marta Kwiatkowska; Yarin Gal (CoRR, 2022)

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas W. Anthony 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 2022)

Learning Correlated Equilibria in Mean-Field Games. - Paul Muller; Romuald Elie; Mark Rowland 001; Mathieu Laurière; Julien Pérolat; Sarah Perrin; Matthieu Geist; Georgios Piliouras; Olivier Pietquin; Karl Tuyls (CoRR, 2022)

Evolutionary Dynamics and Phi-Regret Minimization in Games. - Georgios Piliouras; Mark Rowland 001; Shayegan Omidshafiei; Romuald Elie; Daniel Hennes; Jerome T. Connor; Karl Tuyls (J. Artif. Intell. Res., 2022)

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas Anthony 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. - Charline Le Lan; Joshua Greaves; Jesse Farebrother; Mark Rowland 001; Fabian Pedregosa; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 2023)

Bootstrapped Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Mark Rowland 001; Anna Harutyunyan; Rishabh Agarwal; Marc G. Bellemare; Will Dabney (ICML, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. - Mark Rowland 001; Yunhao Tang; Clare Lyle; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Anna Harutyunyan; Rémi Munos; Bernardo Ávila Pires; Michal Valko (ICML, 2023)

VA-learning as a more efficient alternative to Q-learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Michal Valko (ICML, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. - Pablo Samuel Castro; Tyler Kastner; Prakash Panangaden; Mark Rowland 001 (Trans. Mach. Learn. Res., 2023)

A General Theoretical Paradigm to Understand Learning from Human Preferences. - Mohammad Gheshlaghi Azar; Zhaohan Daniel Guo; Bilal Piot; Rémi Munos; Mark Rowland 001; Michal Valko; Daniele Calandriello (AISTATS, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

Distributional Bellman Operators over Mean Embeddings. - Li Kevin Wenliang; Grégoire Delétang; Matthew Aitchison; Marcus Hutter; Anian Ruoss; Arthur Gretton; Mark Rowland 001 (ICML, 2024)

A Distributional Analogue to the Successor Representation. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Yunhao Tang; André Barreto 001; Will Dabney; Marc G. Bellemare; Mark Rowland 001 (ICML, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Kevin Kevin Li; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (NeurIPS, 2024)

Foundations of Multivariate Distributional Reinforcement Learning. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Mark Rowland 001 (NeurIPS, 2024)

Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Bernardo Ávila Pires; Will Dabney (CoRR, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Li Kevin Wenliang; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (CoRR, 2024)

An Analysis of Quantile Temporal-Difference Learning. - Mark Rowland 001; Rémi Munos; Mohammad Gheshlaghi Azar; Yunhao Tang; Georg Ostrovski; Anna Harutyunyan; Karl Tuyls; Marc G. Bellemare; Will Dabney (J. Mach. Learn. Res., 2024)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Agency Is Frame-Dependent. - David Abel; André Barreto 001; Michael Bowling; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Plasticity as the Mirror of Empowerment. - David Abel; Michael Bowling; André Barreto 001; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)