← Authors

Mark Rowland

52 papers · 2016–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸƒ Academic Marathon (9) 🐝 Cross-Pollinator (9) 🌍 Conference Polyglot (6) πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🏠 Conference Loyalist (24) 🀝 Dynamic Duo (27) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Grand Slam πŸ”¬ Deep Specialist (19) πŸ† Keyword Champion (2) πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (172) πŸ“ˆ Trend Setter πŸ’Ž Century Club (52) πŸ”₯ Unstoppable (10)

Conferences

ICML (24) AISTATS (12) NIPS (10) ICLR (3) JMLR (2) AAAI (1)

Top co-authors

RΓ©mi Munos (27) Will Dabney (21) Yunhao Tang (19) Michal Valko (14) Adrian Weller (9) Clare Lyle (8) Bernardo Avila Pires (7) Zhaohan Daniel Guo (7) Daniele Calandriello (6) Mohammad Gheshlaghi azar (5)

Research topics

Applications (1)

Keywords

deep reinforcement learning (10) reinforcement learning (9) distributional reinforcement learning (8) off-policy learning (6) value function (5) return distribution (5) representation learning (4) policy gradient (4) policy optimization (4) nash equilibrium (3) multi-step learning (3) temporal difference learning (3) variance reduction (3) graphical model (3) game theory (3) kernel approximation (3) importance sampling (2) off-policy evaluation (2) policy evaluation (2) bellman equation (2)

Papers

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning AISTATS 2025
Optimizing Return Distributions with Distributional Dynamic Programming JMLR 2025
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics ICML 2025
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model NIPS 2024
A General Theoretical Paradigm to Understand Learning from Human Preferences AISTATS 2024
Foundations of Multivariate Distributional Reinforcement Learning NIPS 2024
A Distributional Analogue to the Successor Representation ICML 2024
Distributional Bellman Operators over Mean Embeddings ICML 2024
Generalized Preference Optimization: A Unified Approach to Offline Alignment ICML 2024
Nash Learning from Human Feedback ICML 2024
Human Alignment of Large Language Models through Online Preference Optimisation ICML 2024
An Analysis of Quantile Temporal-Difference Learning JMLR 2024
Understanding Self-Predictive Learning for Reinforcement Learning ICML 2023
Quantile Credit Assignment ICML 2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation ICML 2023
VA-learning as a more efficient alternative to Q-learning ICML 2023
Bootstrapped Representations in Reinforcement Learning ICML 2023
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm ICML 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023
Generalised Policy Improvement with Geometric Policy Composition ICML 2022
Learning Dynamics and Generalization in Deep Reinforcement Learning ICML 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees NIPS 2022
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning NIPS 2022
Marginalized Operators for Off-policy Reinforcement Learning AISTATS 2022
Understanding and Preventing Capacity Loss in Reinforcement Learning ICLR 2022
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation NIPS 2021
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning AAAI 2021
MICo: Improved representations via sampling-based state similarity for Markov decision processes NIPS 2021
Taylor Expansion of Discount Factors ICML 2021
From PoincarΓ© Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization ICML 2021
Revisiting Peng’s Q($Ξ»$) for Modern Reinforcement Learning ICML 2021
On the Effect of Auxiliary Tasks on Representation Dynamics AISTATS 2021
Fast computation of Nash Equilibria in Imperfect Information Games ICML 2020
Adaptive Trade-Offs in Off-Policy Learning AISTATS 2020
Conditional Importance Sampling for Off-Policy Learning AISTATS 2020
A Generalized Training Approach for Multiagent Learning ICLR 2020
Revisiting Fundamentals of Experience Replay ICML 2020
Multiagent Evaluation under Incomplete Information NIPS 2019
Unifying Orthogonal Monte Carlo Methods ICML 2019
Orthogonal Estimation of Wasserstein Distances AISTATS 2019
Statistics and Samples in Distributional Reinforcement Learning ICML 2019
The Geometry of Random Features AISTATS 2018
An Analysis of Categorical Distributional Reinforcement Learning AISTATS 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization ICML 2018
Geometrically Coupled Monte Carlo Sampling NIPS 2018
Gaussian Process Behaviour in Wide Deep Neural Networks ICLR 2018
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings NIPS 2017
Magnetic Hamiltonian Monte Carlo ICML 2017
Conditions beyond treewidth for tightness of higher-order LP relaxations AISTATS 2017
Uprooting and Rerooting Higher-Order Graphical Models NIPS 2017
Black-Box Alpha Divergence Minimization ICML 2016
Tightness of LP Relaxations for Almost Balanced Models AISTATS 2016