Mark Rowland - RankMe

111 papers A* 36A 13Journal 59Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. AISTATS Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney
2025	J	`jnl`	Agency Is Frame-Dependent. CoRR David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	A*	`conf`	Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics. ICML Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A. Erdogdu, Amir-massoud Farahmand
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. CoRR Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. J. Mach. Learn. Res. Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Plasticity as the Mirror of Empowerment. CoRR David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2024	A*	`conf`	A Distributional Analogue to the Successor Representation. ICML Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	J	`jnl`	A Distributional Analogue to the Successor Representation. CoRR Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	A	`conf`	A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
2024	J	`jnl`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
2024	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. J. Mach. Learn. Res. Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2024	A*	`conf`	Distributional Bellman Operators over Mean Embeddings. ICML Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
2024	A*	`conf`	Foundations of Multivariate Distributional Reinforcement Learning. NeurIPS Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland
2024	J	`jnl`	Foundations of Multivariate Distributional Reinforcement Learning. CoRR Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland
2024	A*	`conf`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	J	`jnl`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	A*	`conf`	Human Alignment of Large Language Models through Online Preference Optimisation. ICML Daniele Calandriello, Zhaohan Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
2024	J	`jnl`	Human Alignment of Large Language Models through Online Preference Optimisation. CoRR Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
2024	A*	`conf`	Nash Learning from Human Feedback. ICML Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, 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
2024	A*	`conf`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. NeurIPS Mark Rowland, Kevin Kevin Li, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
2023	J	`jnl`	A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
2023	J	`jnl`	A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. CoRR Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
2023	J	`jnl`	A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. Trans. Mach. Learn. Res. Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
2023	A	`conf`	A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. AISTATS Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare
2023	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. CoRR Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Bootstrapped Representations in Reinforcement Learning. ICML Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Bootstrapped Representations in Reinforcement Learning. CoRR Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Distributional Bellman Operators over Mean Embeddings. CoRR Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
2023	A*	`conf`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	J	`jnl`	DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko
2023	J	`jnl`	Nash Learning from Human Feedback. CoRR Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
2023	A*	`conf`	Quantile Credit Assignment. ICML Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
2023	A*	`conf`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
2023	A*	`conf`	VA-learning as a more efficient alternative to Q-learning. ICML Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2023	J	`jnl`	VA-learning as a more efficient alternative to Q-learning. CoRR Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
2022	J	`jnl`	A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. CoRR Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare
2022	J	`jnl`	Evolutionary Dynamics and Phi-Regret Minimization in Games. J. Artif. Intell. Res. Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome T. Connor, Karl Tuyls
2022	A*	`conf`	Generalised Policy Improvement with Geometric Policy Composition. ICML Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
2022	J	`jnl`	Generalised Policy Improvement with Geometric Policy Composition. CoRR Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
2022	J	`jnl`	Learning Correlated Equilibria in Mean-Field Games. CoRR Paul Muller, Romuald Elie, Mark Rowland, Mathieu Laurière, Julien Pérolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls
2022	A*	`conf`	Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	J	`jnl`	Learning Dynamics and Generalization in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	A	`conf`	Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. AAMAS Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls
2022	A	`conf`	Marginalized Operators for Off-policy Reinforcement Learning. AISTATS Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	J	`jnl`	Marginalized Operators for Off-policy Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2022	J	`jnl`	Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls
2022	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	A*	`conf`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
2022	A*	`conf`	Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR Clare Lyle, Mark Rowland, Will Dabney
2022	J	`jnl`	Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney
2021	J	`jnl`	Evolutionary Dynamics and Φ-Regret Minimization in Games. CoRR Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome T. Connor, Karl Tuyls
2021	A*	`conf`	From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
2021	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 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
2021	J	`jnl`	Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. CoRR Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls
2021	A*	`conf`	MICo: Improved representations via sampling-based state similarity for Markov decision processes. NeurIPS Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
2021	J	`jnl`	MICo: Learning improved representations via sampling-based state similarity for Markov decision processes. CoRR Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
2021	J	`jnl`	On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
2021	A	`conf`	On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
2021	A*	`conf`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	J	`jnl`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	A*	`conf`	Taylor Expansion of Discount Factors. ICML Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Taylor Expansion of Discount Factors. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
2021	A*	`conf`	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
2021	A*	`conf`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2021	J	`jnl`	Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
2020	A*	`conf`	A Generalized Training Approach for Multiagent Learning. ICLR Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos
2020	A	`conf`	Adaptive Trade-Offs in Off-Policy Learning. AISTATS Mark Rowland, Will Dabney, Rémi Munos
2020	A	`conf`	Conditional Importance Sampling for Off-Policy Learning. AISTATS Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2020	A*	`conf`	Fast computation of Nash Equilibria in Imperfect Information Games. ICML Rémi Munos, Julien Pérolat, 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
2020	J	`jnl`	From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
2020	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. CoRR 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
2020	J	`jnl`	Navigating the Landscape of Games. CoRR Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome T. Connor, Daniel Hennes, Paul Muller, Julien Pérolat, Bart De Vylder, Audrunas Gruslys, Rémi Munos
2020	A*	`conf`	Revisiting Fundamentals of Experience Replay. ICML William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
2020	J	`jnl`	Revisiting Fundamentals of Experience Replay. CoRR William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
2020	J	`jnl`	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
2019	J	`jnl`	A Generalized Training Approach for Multiagent Learning. CoRR Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos
2019	J	`jnl`	Adaptive Trade-Offs in Off-Policy Learning. CoRR Mark Rowland, Will Dabney, Rémi Munos
2019	J	`jnl`	Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
2019	J	`jnl`	Conditional Importance Sampling for Off-Policy Learning. CoRR Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg
2019	A*	`conf`	Multiagent Evaluation under Incomplete Information. NeurIPS Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
2019	J	`jnl`	Multiagent Evaluation under Incomplete Information. CoRR Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
2019	A	`conf`	Orthogonal Estimation of Wasserstein Distances. AISTATS Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
2019	J	`jnl`	Orthogonal Estimation of Wasserstein Distances. CoRR Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
2019	A*	`conf`	Statistics and Samples in Distributional Reinforcement Learning. ICML Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	J	`jnl`	Statistics and Samples in Distributional Reinforcement Learning. CoRR Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	A*	`conf`	Unifying Orthogonal Monte Carlo Methods. ICML Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller
2019	J	`jnl`	α-Rank: Multi-Agent Evaluation by Evolution. CoRR Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Pérolat, Rémi Munos
2018	A	`conf`	An Analysis of Categorical Distributional Reinforcement Learning. AISTATS Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
2018	J	`jnl`	Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
2018	A*	`conf`	Distributional Reinforcement Learning With Quantile Regression. AAAI Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
2018	—	`conf`	Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani
2018	J	`jnl`	Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018	A*	`conf`	Geometrically Coupled Monte Carlo Sampling. NeurIPS Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller
2018	A*	`conf`	Structured Evolution with Compact Architectures for Scalable Policy Optimization. ICML Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
2018	J	`jnl`	Structured Evolution with Compact Architectures for Scalable Policy Optimization. CoRR Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
2018	A	`conf`	The Geometry of Random Features. AISTATS Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller
2017	A	`conf`	Conditions beyond treewidth for tightness of higher-order LP relaxations. AISTATS Mark Rowland, Aldo Pacchiano, Adrian Weller
2017	J	`jnl`	Distributional Reinforcement Learning with Quantile Regression. CoRR Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
2017	A*	`conf`	Magnetic Hamiltonian Monte Carlo. ICML Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner
2017	—	`conf`	The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. NIPS Krzysztof Marcin Choromanski, Mark Rowland, Adrian Weller
2017	—	`conf`	Uprooting and Rerooting Higher-Order Graphical Models. NIPS Mark Rowland, Adrian Weller
2016	A*	`conf`	Black-Box Alpha Divergence Minimization. ICML José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
2016	A	`conf`	Tightness of LP Relaxations for Almost Balanced Models. AISTATS Adrian Weller, Mark Rowland, David A. Sontag