default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMark Rowland 0001
Person information
- affiliation: Google DeepMind, London, UK
Other persons with the same name
- Mark Rowland 0002 — NUI, Galway, Ireland
- Mark Rowland 0003 — Icergi Ltd., Dublin, Ireland
- Mark Rowland 0004 — PWC
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j5]Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney:
An Analysis of Quantile Temporal-Difference Learning. J. Mach. Learn. Res. 25: 163:1-163:47 (2024)- [c48]Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello:
A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS 2024: 4447-4455 - [c47]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:
Human Alignment of Large Language Models through Online Preference Optimisation. ICML 2024 - [c46]Rémi 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:
Nash Learning from Human Feedback. ICML 2024 - [c45]Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot:
Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML 2024 - [c44]Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland:
Distributional Bellman Operators over Mean Embeddings. ICML 2024 - [c43]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. ICML 2024 - [i50]Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot:
Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR abs/2402.05749 (2024) - [i49]Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney:
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR abs/2402.05766 (2024) - [i48]Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney:
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR abs/2402.07598 (2024) - [i47]Harley Wiltzer
, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. CoRR abs/2402.08530 (2024) - [i46]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:
Human Alignment of Large Language Models through Online Preference Optimisation. CoRR abs/2403.08635 (2024) - [i45]Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney:
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR abs/2406.02035 (2024) - [i44]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland:
Foundations of Multivariate Distributional Reinforcement Learning. CoRR abs/2409.00328 (2024) - 2023
- [j4]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. Trans. Mach. Learn. Res. 2023 (2023) - [c42]Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. AISTATS 2023: 1703-1718 - [c41]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. ICML 2023: 18686-18713 - [c40]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:
Quantile Credit Assignment. ICML 2023: 24517-24531 - [c39]Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney:
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML 2023: 29210-29231 - [c38]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:
Understanding Self-Predictive Learning for Reinforcement Learning. ICML 2023: 33632-33656 - [c37]Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko:
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. ICML 2023: 33657-33673 - [c36]Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko:
VA-learning as a more efficient alternative to Q-learning. ICML 2023: 33739-33757 - [i43]Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney:
An Analysis of Quantile Temporal-Difference Learning. CoRR abs/2301.04462 (2023) - [i42]Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko:
VA-learning as a more efficient alternative to Q-learning. CoRR abs/2305.18161 (2023) - [i41]Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney:
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR abs/2305.18388 (2023) - [i40]Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko:
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. CoRR abs/2305.18501 (2023) - [i39]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. CoRR abs/2306.10171 (2023) - [i38]Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos:
A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR abs/2310.12036 (2023) - [i37]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. CoRR abs/2310.19804 (2023) - [i36]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:
Nash Learning from Human Feedback. CoRR abs/2312.00886 (2023) - [i35]Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland:
Distributional Bellman Operators over Mean Embeddings. CoRR abs/2312.07358 (2023) - 2022
- [j3]Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome T. Connor, Karl Tuyls:
Evolutionary Dynamics and Phi-Regret Minimization in Games. J. Artif. Intell. Res. 74: 1125-1158 (2022) - [c35]Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko:
Marginalized Operators for Off-policy Reinforcement Learning. AISTATS 2022: 655-679 - [c34]Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls:
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. AAMAS 2022: 926-934 - [c33]Clare Lyle, Mark Rowland, Will Dabney:
Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR 2022 - [c32]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML 2022: 14560-14581 - [c31]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. ICML 2022: 21272-21307 - [c30]Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare:
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS 2022 - [c29]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS 2022 - [d1]Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas 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:
Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". Zenodo, 2022
- [i34]Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko:
Marginalized Operators for Off-policy Reinforcement Learning. CoRR abs/2203.16177 (2022) - [i33]Clare Lyle, Mark Rowland, Will Dabney:
Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR abs/2204.09560 (2022) - [i32]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Reinforcement Learning. CoRR abs/2206.02126 (2022) - [i31]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. CoRR abs/2206.08736 (2022) - [i30]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:
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR abs/2206.15378 (2022) - [i29]Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare:
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR abs/2207.07570 (2022) - [i28]Paul Muller, Romuald Elie, Mark Rowland, Mathieu Laurière, Julien Pérolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls:
Learning Correlated Equilibria in Mean-Field Games. CoRR abs/2208.10138 (2022) - [i27]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR abs/2209.14414 (2022) - [i26]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:
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR abs/2212.03319 (2022) - [i25]Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. CoRR abs/2212.04025 (2022) - 2021
- [j2]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:
Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 71: 41-88 (2021) - [c28]Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver:
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI 2021: 7160-7168 - [c27]Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS 2021: 1-9 - [c26]Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel:
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML 2021: 5794-5804 - [c25]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:
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML 2021: 8525-8535 - [c24]Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko:
Taylor Expansion of Discount Factors. ICML 2021: 10130-10140 - [c23]Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko:
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS 2021: 5303-5315 - [c22]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
MICo: Improved representations via sampling-based state similarity for Markov decision processes. NeurIPS 2021: 30113-30126 - [i24]Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR abs/2102.13089 (2021) - [i23]Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel:
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR abs/2103.00107 (2021) - [i22]Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko:
Taylor Expansion of Discount Factors. CoRR abs/2106.06170 (2021) - [i21]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
MICo: Learning improved representations via sampling-based state similarity for Markov decision processes. CoRR abs/2106.08229 (2021) - [i20]Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko:
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR abs/2106.13125 (2021) - [i19]Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome T. Connor, Karl Tuyls:
Evolutionary Dynamics and Φ-Regret Minimization in Games. CoRR abs/2106.14668 (2021) - [i18]Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls:
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. CoRR abs/2111.08350 (2021) - 2020
- [c21]Mark Rowland, Will Dabney, Rémi Munos:
Adaptive Trade-Offs in Off-Policy Learning. AISTATS 2020: 34-44 - [c20]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. AISTATS 2020: 45-55 - [c19]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:
A Generalized Training Approach for Multiagent Learning. ICLR 2020 - [c18]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. ICML 2020: 3061-3071 - [c17]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:
Fast computation of Nash Equilibria in Imperfect Information Games. ICML 2020: 7119-7129 - [i17]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:
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR abs/2002.08456 (2020) - [i16]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:
Navigating the Landscape of Games. CoRR abs/2005.01642 (2020) - [i15]Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver:
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR abs/2006.02243 (2020) - [i14]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. CoRR abs/2007.06700 (2020) - [i13]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:
Game Plan: What AI can do for Football, and What Football can do for AI. CoRR abs/2011.09192 (2020)
2010 – 2019
- 2019
- [j1]Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani:
Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. 29(5): 1127-1147 (2019) - [c16]Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller:
Orthogonal Estimation of Wasserstein Distances. AISTATS 2019: 186-195 - [c15]Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller:
Unifying Orthogonal Monte Carlo Methods. ICML 2019: 1203-1212 - [c14]Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. ICML 2019: 5528-5536 - [c13]Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos:
Multiagent Evaluation under Incomplete Information. NeurIPS 2019: 12270-12282 - [i12]Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. CoRR abs/1902.08102 (2019) - [i11]Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Pérolat, Rémi Munos:
α-Rank: Multi-Agent Evaluation by Evolution. CoRR abs/1903.01373 (2019) - [i10]Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller:
Orthogonal Estimation of Wasserstein Distances. CoRR abs/1903.03784 (2019) - [i9]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i8]Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos:
Multiagent Evaluation under Incomplete Information. CoRR abs/1909.09849 (2019) - [i7]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:
A Generalized Training Approach for Multiagent Learning. CoRR abs/1909.12823 (2019) - [i6]Mark Rowland, Will Dabney, Rémi Munos:
Adaptive Trade-Offs in Off-Policy Learning. CoRR abs/1910.07478 (2019) - [i5]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. CoRR abs/1910.07479 (2019) - 2018
- [c12]Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos:
Distributional Reinforcement Learning With Quantile Regression. AAAI 2018: 2892-2901 - [c11]Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
The Geometry of Random Features. AISTATS 2018: 1-9 - [c10]Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh:
An Analysis of Categorical Distributional Reinforcement Learning. AISTATS 2018: 29-37 - [c9]Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) 2018 - [c8]Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. ICML 2018: 969-977 - [c7]Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller:
Geometrically Coupled Monte Carlo Sampling. NeurIPS 2018: 195-205 - [i4]Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. CoRR abs/1804.02395 (2018) - [i3]Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR abs/1804.11271 (2018) - [i2]Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani:
Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR abs/1807.00400 (2018) - 2017
- [c6]Mark Rowland, Aldo Pacchiano, Adrian Weller:
Conditions beyond treewidth for tightness of higher-order LP relaxations. AISTATS 2017: 10-18 - [c5]Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner:
Magnetic Hamiltonian Monte Carlo. ICML 2017: 3453-3461 - [c4]Mark Rowland, Adrian Weller:
Uprooting and Rerooting Higher-Order Graphical Models. NIPS 2017: 209-218 - [c3]Krzysztof Marcin Choromanski, Mark Rowland, Adrian Weller:
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. NIPS 2017: 219-228 - [i1]Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos:
Distributional Reinforcement Learning with Quantile Regression. CoRR abs/1710.10044 (2017) - 2016
- [c2]Adrian Weller, Mark Rowland, David A. Sontag:
Tightness of LP Relaxations for Almost Balanced Models. AISTATS 2016: 47-55 - [c1]José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. ICML 2016: 1511-1520
Coauthor Index
[j5] [c48] [c47] [c46] [c45] [i50] [i49] [i48] [i46] [c40] [c39] [c38] [c37] [c36] [i43] [i42] [i41] [i40] [i38] [i36] [c35] [c31] [c30] [c29] [d1] [i34] [i31] [i30] [i29] [i27] [i26] [j2] [c26] [c25] [c24] [c23] [i23] [i22] [i20] [c21] [c20] [c19] [c17] [i17] [i16] [i13] [c14] [c13] [i12] [i11] [i8] [i7] [i6] [i5] [c12] [c10] [i1]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-30 20:35 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
