Jesse Farebrother

11 papers · 2023–2025 · 4 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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🌉 Interdisciplinary Bridge · Bronze 🌍 Conference Polyglot · Bronze 🗺️ Taxonomy Completionist (18) 🐣 Hot Topic Early Bird 🗃️ Keyword Collector (56)

⚡ Prolific Year · Productive 💎 Century Club · Bronze

Venues

ICLR (4) ICML (4) NeurIPS (2) AISTATS (1)

Top co-authors

Rishabh Agarwal (4) Pablo Samuel Castro (4) Mark Rowland (3) Marc G Bellemare (3) Harley Wiltzer (3) Joshua Greaves (2) Matteo Pirotta (2) Adrien Ali Taiga (2) Alessandro Lazaric (2) Charline Le Lan (2)

Research topics

Machine Learning (6) Reinforcement Learning (6) Artificial Intelligence (5) Methods (5) Core Methods (4) Core AI (3) Deep Learning (2) Applications (2) Learning Paradigms (2) Learning Types · Machine Learning (2) Optimization & Theory (2) Learning Types · Deep Learning (1) Models (1)

Keywords

reinforcement learning (3) representation learning (2) imitation learning (2) policy gradient (2) flow matching (1) multi-task learning (1) temporal difference learning (1) matrix factorization (1) stochastic gradient descent (1) value function (1) bellman equation (1) distributional reinforcement learning (1) inverse reinforcement learning (1) behavior cloning (1) motion capture (1) transfer learning (1) dimensionality reduction (1) dynamic programming (1) nonstationarity (1) deep reinforcement learning (1)

Papers

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching ICLR 2025 Temporal Difference Flows ICML 2025 Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models ICLR 2025 A Distributional Analogue to the Successor Representation ICML 2024 CALE: Continuous Arcade Learning Environment NeurIPS 2024 Foundations of Multivariate Distributional Reinforcement Learning NeurIPS 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL ICML 2024 Stop Regressing: Training Value Functions via Classification for Scalable Deep RL ICML 2024 A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023 Investigating Multi-task Pretraining and Generalization in Reinforcement Learning ICLR 2023 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks ICLR 2023