Mark Rowland

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 Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization ICML 2021 Revisiting Peng’s Q($λ$) 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