Rowland, Mark

62 publications

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

NeurIPS 2025 Capturing Individual Human Preferences with Reward Features Andre Barreto, Vincent Dumoulin, Yiran Mao, Mark Rowland, Nicolas Perez-Nieves, Bobak Shahriari, Yann Dauphin, Doina Precup, Hugo Larochelle

ICML 2025 Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A Erdogdu, Amir-Massoud Farahmand

JMLR 2025 Optimizing Return Distributions with Distributional Dynamic Programming Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney

NeurIPS 2025 Plasticity as the Mirror of Empowerment David Abel, Michael Bowling, Andre Barreto, Will Dabney, Shi Dong, Steven Stenberg Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

ICML 2024 A Distributional Analogue to the Successor Representation Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andre Barreto, Will Dabney, Marc G Bellemare, Mark Rowland

AISTATS 2024 A General Theoretical Paradigm to Understand Learning from Human Preferences Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, Daniele Calandriello

NeurIPSW 2024 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

JMLR 2024 An Analysis of Quantile Temporal-Difference Learning Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

ICML 2024 Distributional Bellman Operators over Mean Embeddings Li Kevin Wenliang, Gregoire Deletang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland

NeurIPS 2024 Foundations of Multivariate Distributional Reinforcement Learning Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland

ICML 2024 Generalized Preference Optimization: A Unified Approach to Offline Alignment Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Remi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Avila Pires, Bilal Piot

ICML 2024 Human Alignment of Large Language Models Through Online Preference Optimisation Daniele Calandriello, Zhaohan Daniel Guo, Remi Munos, Mark Rowland, Yunhao Tang, Bernardo Avila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot

ICML 2024 Nash Learning from Human Feedback Remi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J Mankowitz, Doina Precup, Bilal Piot

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

TMLR 2023 A Kernel Perspective on Behavioural Metrics for Markov Decision Processes Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

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

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

ICLRW 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

ICML 2023 DoMo-AC: Doubly Multi-Step Off-Policy Actor-Critic Algorithm Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Remi Munos, Bernardo Avila Pires, Michal Valko

ICML 2023 Quantile Credit Assignment Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Remi Munos

ICML 2023 The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation Mark Rowland, Yunhao Tang, Clare Lyle, Remi Munos, Marc G Bellemare, Will Dabney

ICML 2023 Understanding Self-Predictive Learning for Reinforcement Learning Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Avila Pires, Yash Chandak, Remi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

ICML 2023 VA-Learning as a More Efficient Alternative to Q-Learning Yunhao Tang, Remi Munos, Mark Rowland, Michal Valko

AISTATS 2022 Marginalized Operators for Off-Policy Reinforcement Learning Yunhao Tang, Mark Rowland, Remi Munos, Michal Valko

NeurIPSW 2022 A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G Bellemare

JAIR 2022 Evolutionary Dynamics and Phi-Regret Minimization in Games Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome T. Connor, Karl Tuyls

ICML 2022 Generalised Policy Improvement with Geometric Policy Composition Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Remi Munos, Andre Barreto

ICML 2022 Learning Dynamics and Generalization in Deep Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

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

NeurIPS 2022 The Nature of Temporal Difference Errors in Multi-Step Distributional Reinforcement Learning Yunhao Tang, Remi Munos, Mark Rowland, Bernardo Avila Pires, Will Dabney, Marc Bellemare

ICLR 2022 Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney

AISTATS 2021 On the Effect of Auxiliary Tasks on Representation Dynamics Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

ICML 2021 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls

JAIR 2021 Game Plan: What AI Can Do for Football, and What Football Can Do for AI Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis

NeurIPS 2021 MICo: Improved Representations via Sampling-Based State Similarity for Markov Decision Processes Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

ICML 2021 Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning Tadashi Kozuno, Yunhao Tang, Mark Rowland, Remi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

ICML 2021 Taylor Expansion of Discount Factors Yunhao Tang, Mark Rowland, Remi Munos, Michal Valko

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

NeurIPSW 2021 Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney

NeurIPS 2021 Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation Yunhao Tang, Tadashi Kozuno, Mark Rowland, Remi Munos, Michal Valko

ICLR 2020 A Generalized Training Approach for Multiagent Learning Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos

AISTATS 2020 Adaptive Trade-Offs in Off-Policy Learning Mark Rowland, Will Dabney, Remi Munos

AISTATS 2020 Conditional Importance Sampling for Off-Policy Learning Mark Rowland, Anna Harutyunyan, Hado Hasselt, Diana Borsa, Tom Schaul, Remi Munos, Will Dabney

ICML 2020 Fast Computation of Nash Equilibria in Imperfect Information Games Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls

ICML 2020 Revisiting Fundamentals of Experience Replay William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

NeurIPS 2019 Multiagent Evaluation Under Incomplete Information Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos

AISTATS 2019 Orthogonal Estimation of Wasserstein Distances Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller

ICML 2019 Statistics and Samples in Distributional Reinforcement Learning Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney

ICML 2019 Unifying Orthogonal Monte Carlo Methods Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller

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

AAAI 2018 Distributional Reinforcement Learning with Quantile Regression Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos

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

NeurIPS 2018 Geometrically Coupled Monte Carlo Sampling Mark Rowland, Krzysztof M Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E Turner, Adrian Weller

ICML 2018 Structured Evolution with Compact Architectures for Scalable Policy Optimization Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard Turner, Adrian Weller

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

AISTATS 2017 Conditions Beyond Treewidth for Tightness of Higher-Order LP Relaxations Mark Rowland, Aldo Pacchiano, Adrian Weller

ICML 2017 Magnetic Hamiltonian Monte Carlo Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

NeurIPS 2017 The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings Krzysztof M Choromanski, Mark Rowland, Adrian Weller

NeurIPS 2017 Uprooting and Rerooting Higher-Order Graphical Models Mark Rowland, Adrian Weller

ICML 2016 Black-Box Alpha Divergence Minimization Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner

AISTATS 2016 Tightness of LP Relaxations for Almost Balanced Models Adrian Weller, Mark Rowland, David A. Sontag