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← Authors

Clare Lyle

23 papers · 2019–2025 · 6 conferences · across top CS/AI conferences

Achievements

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+12 more ↓ 🌍 Conference Polyglot (6) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸƒ Academic Marathon (6)
🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (39) 🀝 Dynamic Duo (12) πŸ† Grand Slam πŸ”¬ Deep Specialist (11) πŸ’Ž Century Club (23) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (94) πŸ“ˆ Trend Setter ⚑ Prolific Year (5) πŸ”₯ Unstoppable (7)

Conferences

ICML (10) NIPS (8) AISTATS (2) AAAI (1) ICLR (1) IJCAI (1)

Top co-authors

Will Dabney (12) Mark Rowland (8) Yarin Gal (7) Yunhao Tang (5) RΓ©mi Munos (4) Razvan Pascanu (4) Pablo Samuel Castro (3) Georg Ostrovski (3) Marta Kwiatkowska (3) Bernardo Avila Pires (3)

Keywords

deep reinforcement learning (4) distributional reinforcement learning (4) representation learning (3) reinforcement learning (3) continual learning (2) causal inference (2) neural network plasticity (2) return distribution (2) value estimation (2) neural network (2) auxiliary task (2) spectral decomposition (2) policy gradient (1) bayesian inference (1) imitation learning (1) model selection (1) temporal difference learning (1) self-attention mechanism (1) minimax optimization (1) state abstraction (1)

Papers

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning AISTATS 2025 Normalization and effective learning rates in reinforcement learning NIPS 2024 Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model NIPS 2024 Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL ICML 2024 Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset NIPS 2024 Deep Reinforcement Learning with Plasticity Injection NIPS 2023 DiscoBAX: Discovery of optimal intervention sets in genomic experiment design ICML 2023 Understanding Plasticity in Neural Networks ICML 2023 Quantile Credit Assignment ICML 2023 The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation ICML 2023 Understanding Self-Predictive Learning for Reinforcement Learning ICML 2023 Learning Dynamics and Generalization in Deep Reinforcement Learning ICML 2022 Understanding and Preventing Capacity Loss in Reinforcement Learning ICLR 2022 Provable Guarantees on the Robustness of Decision Rules to Causal Interventions IJCAI 2021 Speedy Performance Estimation for Neural Architecture Search NIPS 2021 Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning NIPS 2021 On the Effect of Auxiliary Tasks on Representation Dynamics AISTATS 2021 PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning ICML 2021 Invariant Causal Prediction for Block MDPs ICML 2020 A Bayesian Perspective on Training Speed and Model Selection NIPS 2020 A Comparative Analysis of Expected and Distributional Reinforcement Learning AAAI 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning NIPS 2019