Pablo Samuel Castro
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
29
Venues
7
Active years
2006–2025
Best venue rank
A*
Where they publish
Papers
29 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning. | Samuel Garcin, Trevor McInroe, Pablo Samuel Castro, Christopher G. Lucas, David Abel, Prakash Panangaden, Stefano V. Albrecht |
| 2025 | ICLR | Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL. | Ghada Sokar, Johan S. Obando-Ceron, Aaron C. Courville, Hugo Larochelle, Pablo Samuel Castro |
| 2025 | ICML | Discovering Symbolic Cognitive Models from Human and Animal Behavior. | Pablo Samuel Castro, Nenad Tomasev, Ankit Anand, Navodita Sharma, Rishika Mohanta, Aparna Dev, Kuba Perlin, Siddhant Jain, Kyle Levin, Nomi lteto, Will Dabney, Alexander Novikov, Glenn C. Turner, Maria K. Eckstein, Nathaniel D. Daw, Kevin J. Miller, Kim Stachenfeld |
| 2025 | ICML | The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning. | Jiashun Liu, Johan S. Obando-Ceron, Pablo Samuel Castro, Aaron C. Courville, Ling Pan |
| 2025 | ICML | The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks. | Walter Mayor, Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro |
| 2025 | ICML | Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn. | Hongyao Tang, Johan S. Obando-Ceron, Pablo Samuel Castro, Aaron C. Courville, Glen Berseth |
| 2024 | ICML | Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. | Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal |
| 2024 | ICML | In value-based deep reinforcement learning, a pruned network is a good network. | Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro |
| 2024 | ICML | Mixtures of Experts Unlock Parameter Scaling for Deep RL. | Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro |
| 2024 | ICML | Adaptive Accompaniment with ReaLchords. | Yusong Wu, Tim Cooijmans, Kyle Kastner, Adam Roberts, Ian Simon, Alexander Scarlatos, Chris Donahue, Cassie Tarakajian, Shayegan Omidshafiei, Aaron C. Courville, Pablo Samuel Castro, Natasha Jaques, Cheng-Zhi Anna Huang |
| 2023 | ICLR | Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. | Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare |
| 2023 | ICLR | The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. | Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro |
| 2023 | ICML | Bigger, Better, Faster: Human-level Atari with human-level efficiency. | Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro |
| 2023 | ICML | The Dormant Neuron Phenomenon in Deep Reinforcement Learning. | Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci |
| 2022 | AISTATS | A general class of surrogate functions for stable and efficient reinforcement learning. | Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux |
| 2022 | ICML | The State of Sparse Training in Deep Reinforcement Learning. | Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro |
| 2021 | AAAI | Metrics and Continuity in Reinforcement Learning. | Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro |
| 2021 | ICLR | Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. | Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare |
| 2021 | ICML | Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. | Johan S. Obando-Ceron, Pablo Samuel Castro |
| 2020 | AAAI | Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes. | Pablo Samuel Castro |
| 2020 | ICML | Rigging the Lottery: Making All Tickets Winners. | Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen |
| 2019 | AAAI | A Comparative Analysis of Expected and Distributional Reinforcement Learning. | Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro |
| 2019 | AISTATS | Distributional reinforcement learning with linear function approximation. | Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra |
| 2019 | IJCAI | An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. | Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman |
| 2011 | Mobiquitous | Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces. | Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, Shijian Li |
| 2010 | AAAI | Using Bisimulation for Policy Transfer in MDPs. | Pablo Samuel Castro, Doina Precup |
| 2009 | IJCAI | Equivalence Relations in Fully and Partially Observable Markov Decision Processes. | Pablo Samuel Castro, Prakash Panangaden, Doina Precup |
| 2007 | IJCAI | Using Linear Programming for Bayesian Exploration in Markov Decision Processes. | Pablo Samuel Castro, Doina Precup |
| 2006 | UAI | Methods for Computing State Similarity in Markov Decision Processes. | Norm Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden |