Mark Rowland
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
41
Venues
4
Active years
2016–2025
Best venue rank
A*
Where they publish
Papers
41 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. | Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo vila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney |
| 2025 | ICML | Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics. | Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A. Erdogdu, Amir-massoud Farahmand |
| 2024 | AISTATS | A General Theoretical Paradigm to Understand Learning from Human Preferences. | Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rmi Munos, Mark Rowland, Michal Valko, Daniele Calandriello |
| 2024 | ICML | Human Alignment of Large Language Models through Online Preference Optimisation. | Daniele Calandriello, Zhaohan Daniel Guo, Rmi Munos, Mark Rowland, Yunhao Tang, Bernardo vila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot |
| 2024 | ICML | Nash Learning from Human Feedback. | Rmi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Cme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot |
| 2024 | ICML | Generalized Preference Optimization: A Unified Approach to Offline Alignment. | Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rmi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo vila Pires, Bilal Piot |
| 2024 | ICML | Distributional Bellman Operators over Mean Embeddings. | Li Kevin Wenliang, Grgoire Deltang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland |
| 2024 | ICML | A Distributional Analogue to the Successor Representation. | Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andr Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland |
| 2023 | AISTATS | 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 |
| 2023 | ICML | Bootstrapped Representations in Reinforcement Learning. | Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney |
| 2023 | ICML | 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, Rmi Munos |
| 2023 | ICML | The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. | Mark Rowland, Yunhao Tang, Clare Lyle, Rmi Munos, Marc G. Bellemare, Will Dabney |
| 2023 | ICML | Understanding Self-Predictive Learning for Reinforcement Learning. | Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo vila Pires, Yash Chandak, Rmi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, Andrs Gyrgy, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko |
| 2023 | ICML | DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. | Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rmi Munos, Bernardo vila Pires, Michal Valko |
| 2023 | ICML | VA-learning as a more efficient alternative to Q-learning. | Yunhao Tang, Rmi Munos, Mark Rowland, Michal Valko |
| 2022 | AISTATS | Marginalized Operators for Off-policy Reinforcement Learning. | Yunhao Tang, Mark Rowland, Rmi Munos, Michal Valko |
| 2022 | ICLR | Understanding and Preventing Capacity Loss in Reinforcement Learning. | Clare Lyle, Mark Rowland, Will Dabney |
| 2022 | ICML | Learning Dynamics and Generalization in Deep Reinforcement Learning. | Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal |
| 2022 | ICML | Generalised Policy Improvement with Geometric Policy Composition. | Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rmi Munos, Andr Barreto |
| 2021 | AAAI | The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. | Will Dabney, Andr Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver |
| 2021 | AISTATS | On the Effect of Auxiliary Tasks on Representation Dynamics. | Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney |
| 2021 | ICML | Revisiting Peng's Q(λ) for Modern Reinforcement Learning. | Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rmi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel |
| 2021 | ICML | From Poincar Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. | Julien Prolat, Rmi 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 | ICML | Taylor Expansion of Discount Factors. | Yunhao Tang, Mark Rowland, Rmi Munos, Michal Valko |
| 2020 | AISTATS | Adaptive Trade-Offs in Off-Policy Learning. | Mark Rowland, Will Dabney, Rmi Munos |
| 2020 | AISTATS | Conditional Importance Sampling for Off-Policy Learning. | Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rmi Munos, Will Dabney |
| 2020 | ICLR | A Generalized Training Approach for Multiagent Learning. | Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Prolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rmi Munos |
| 2020 | ICML | Revisiting Fundamentals of Experience Replay. | William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney |
| 2020 | ICML | Fast computation of Nash Equilibria in Imperfect Information Games. | Rmi Munos, Julien Prolat, 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 |
| 2019 | AISTATS | Orthogonal Estimation of Wasserstein Distances. | Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tams Sarls, Adrian Weller |
| 2019 | ICML | Unifying Orthogonal Monte Carlo Methods. | Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller |
| 2019 | ICML | Statistics and Samples in Distributional Reinforcement Learning. | Mark Rowland, Robert Dadashi, Saurabh Kumar, Rmi Munos, Marc G. Bellemare, Will Dabney |
| 2018 | AAAI | Distributional Reinforcement Learning With Quantile Regression. | Will Dabney, Mark Rowland, Marc G. Bellemare, Rmi Munos |
| 2018 | AISTATS | The Geometry of Random Features. | Krzysztof Choromanski, Mark Rowland, Tams Sarls, Vikas Sindhwani, Richard E. Turner, Adrian Weller |
| 2018 | AISTATS | An Analysis of Categorical Distributional Reinforcement Learning. | Mark Rowland, Marc G. Bellemare, Will Dabney, Rmi Munos, Yee Whye Teh |
| 2018 | ICLR | Gaussian Process Behaviour in Wide Deep Neural Networks. | Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani |
| 2018 | ICML | Structured Evolution with Compact Architectures for Scalable Policy Optimization. | Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller |
| 2017 | AISTATS | Conditions beyond treewidth for tightness of higher-order LP relaxations. | Mark Rowland, Aldo Pacchiano, Adrian Weller |
| 2017 | ICML | Magnetic Hamiltonian Monte Carlo. | Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner |
| 2016 | AISTATS | Tightness of LP Relaxations for Almost Balanced Models. | Adrian Weller, Mark Rowland, David A. Sontag |
| 2016 | ICML | Black-Box Alpha Divergence Minimization. | Jos Miguel Hernndez-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernndez-Lobato, Richard E. Turner |