conftrace_

Pablo Samuel Castro

31 papers · 2019–2025 · 6 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

Jump to papers ↓

+9 more ↓

🗺️ Taxonomy Completionist (35) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird

👑 Triple Crown 🏆 Keyword Champion (2) 🏆 Grand Slam 🔬 Deep Specialist (20) 🚀 Conference Pioneer 🔥 Unstoppable · Bronze ⚡ Prolific Year · Productive 💎 Century Club · Bronze 🗃️ Keyword Collector (134)

Venues

ICML (13) NeurIPS (8) ICLR (4) AAAI (3) AISTATS (2) IJCAI (1)

Top co-authors

Aaron Courville (7) Rishabh Agarwal (7) Johan Samir Obando Ceron (5) Jesse Farebrother (4) Marc G. Bellemare (4) Marc Bellemare (4) Nicolas Le Roux (3) Max Schwarzer (3) Johan Obando-Ceron (3) Marc G Bellemare (3)

Research topics

Machine Learning (26) Reinforcement Learning (24) Methods (21) Optimization & Theory · Machine Learning (12) Deep Learning (11) Learning Types · Machine Learning (10) Artificial Intelligence (9) Core Methods (9) Core AI (8) Applications (6) Learning Types · Deep Learning (6) Application Areas (4) Architectures (3) Optimization & Theory · Deep Learning (3) Learning Paradigms (2)

Keywords

deep reinforcement learning (9) reinforcement learning (9) representation learning (6) state similarity (3) markov decision process (3) neural network (3) auxiliary task (2) value-based learning (2) sparse neural network (2) benchmark evaluation (2) value iteration (2) sparse training (2) parameter efficiency (2) transfer learning (2) convergence guarantee (2) distributional reinforcement learning (2) policy gradient (2) sample efficiency (2) value function (2) atari game (2)

Papers

Discovering Symbolic Cognitive Models from Human and Animal Behavior ICML 2025 Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL ICLR 2025 Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn ICML 2025 Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning ICLR 2025 The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning ICML 2025 The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks ICML 2025 Adaptive Accompaniment with ReaLchords ICML 2024 CALE: Continuous Arcade Learning Environment NeurIPS 2024 In value-based deep reinforcement learning, a pruned network is a good network ICML 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 Bigger, Better, Faster: Human-level Atari with human-level efficiency ICML 2023 Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks NeurIPS 2023 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks ICLR 2023 Small batch deep reinforcement learning NeurIPS 2023 The Dormant Neuron Phenomenon in Deep Reinforcement Learning ICML 2023 A general class of surrogate functions for stable and efficient reinforcement learning AISTATS 2022 Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress NeurIPS 2022 The State of Sparse Training in Deep Reinforcement Learning ICML 2022 Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning ICLR 2021 Deep Reinforcement Learning at the Edge of the Statistical Precipice NeurIPS 2021 Metrics and Continuity in Reinforcement Learning AAAI 2021 MICo: Improved representations via sampling-based state similarity for Markov decision processes NeurIPS 2021 Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research ICML 2021 The Difficulty of Passive Learning in Deep Reinforcement Learning NeurIPS 2021 Rigging the Lottery: Making All Tickets Winners ICML 2020 Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes AAAI 2020 A Comparative Analysis of Expected and Distributional Reinforcement Learning AAAI 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning NeurIPS 2019 An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents IJCAI 2019 Distributional reinforcement learning with linear function approximation AISTATS 2019