Mark Rowland

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 97

Top Topics

Imperfect Information

Markov Decision Process

Reinforcement Learning

Top Venues

Publications

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 (2024)
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 (2024)
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)
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 (2024)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR (2024)
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 (2024)
Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR (2024)
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 (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. CoRR (2023)
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 (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko
VA-learning as a more efficient alternative to Q-learning. ICML (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. CoRR (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. ICML (2023)
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)
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)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. CoRR (2023)
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)
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)
Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
Distributional Bellman Operators over Mean Embeddings. CoRR (2023)
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 (2023)
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)
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 (2023)
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)
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 (2023)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. CoRR (2022)
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)
Clare Lyle, Mark Rowland, Will Dabney
Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. ICML (2022)
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)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Marginalized Operators for Off-policy Reinforcement Learning. AISTATS (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. CoRR (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Reinforcement Learning. CoRR (2022)
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)
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 (2022)
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 (2022)
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 (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML (2022)
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 (2022)
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 (2022)
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 (2022)
Mark Rowland, Noel Carroll, Kieran Conboy
Examining the Adoption of Artificial Intelligence for Digital Transformations. ECIS (2022)
Clare Lyle, Mark Rowland, Will Dabney
Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR (2022)
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 (2022)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
MICo: Learning improved representations via sampling-based state similarity for Markov decision processes. CoRR (2021)
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS (2021)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. NeurIPS (2021)
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 (2021)
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)
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 (2021)
Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome T. Connor, Karl Tuyls
Evolutionary Dynamics and Φ-Regret Minimization in Games. CoRR (2021)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. ICML (2021)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
MICo: Improved representations via sampling-based state similarity for Markov decision processes. NeurIPS (2021)
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)
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Rémi Munos, Michal Valko
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. CoRR (2021)
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 (2021)
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)
Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
Taylor Expansion of Discount Factors. CoRR (2021)
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR (2021)
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
Revisiting Fundamentals of Experience Replay. ICML (2020)
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)
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)
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 (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. AISTATS (2020)
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 (2020)
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 (2020)
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 (2020)
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
Revisiting Fundamentals of Experience Replay. CoRR (2020)
Mark Rowland, Will Dabney, Rémi Munos
Adaptive Trade-Offs in Off-Policy Learning. AISTATS (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. CoRR (2019)
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
Multiagent Evaluation under Incomplete Information. NeurIPS (2019)
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 (2019)
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
Orthogonal Estimation of Wasserstein Distances. CoRR (2019)
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. 29 (5) (2019)
Mark Rowland, Will Dabney, Rémi Munos
Adaptive Trade-Offs in Off-Policy Learning. CoRR (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. ICML (2019)
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamás Sarlós, Adrian Weller
Orthogonal Estimation of Wasserstein Distances. AISTATS (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. CoRR (2019)
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos
Multiagent Evaluation under Incomplete Information. CoRR (2019)
Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller
Unifying Orthogonal Monte Carlo Methods. ICML (2019)
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 Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
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 (2019)
Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller
Geometrically Coupled Monte Carlo Sampling. NeurIPS (2018)
Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller
The Geometry of Random Features. AISTATS (2018)
Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
An Analysis of Categorical Distributional Reinforcement Learning. AISTATS (2018)
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Distributional Reinforcement Learning With Quantile Regression. AAAI (2018)
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR (2018)
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)
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
Structured Evolution with Compact Architectures for Scalable Policy Optimization. CoRR (2018)
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
Structured Evolution with Compact Architectures for Scalable Policy Optimization. ICML (2018)
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR (2018)
Mark Rowland, Aldo Pacchiano, Adrian Weller
Conditions beyond treewidth for tightness of higher-order LP relaxations. AISTATS (2017)
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner
Magnetic Hamiltonian Monte Carlo. ICML (2017)
Mark Rowland, Adrian Weller
Uprooting and Rerooting Higher-Order Graphical Models. NIPS (2017)
Krzysztof Marcin Choromanski, Mark Rowland, Adrian Weller
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. NIPS (2017)
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Distributional Reinforcement Learning with Quantile Regression. CoRR (2017)
Adrian Weller, Mark Rowland, David A. Sontag
Tightness of LP Relaxations for Almost Balanced Models. AISTATS (2016)
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
Black-Box Alpha Divergence Minimization. ICML (2016)
Jeff Gilbert, Mark Rowland
The Intel® Xeon® processor E5 family architecture, power efficiency, and performance. Hot Chips Symposium (2012)
Mark Rowland
Using systems thinking to better understand the implications of e-business strategies. ICSTM (2000)