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ORCIDPablo Samuel Castro
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2020 – today
- 2024
[j8]Johan Samir Obando-Ceron, João Guilherme Madeira Araújo, Aaron C. Courville, Pablo Samuel Castro:
On the consistency of hyper-parameter selection in value-based deep reinforcement learning. RLJ 3: 1037-1059 (2024)- [j7]Timon Willi, Johan Samir Obando-Ceron, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Pablo Samuel Castro:
Mixture of Experts in a Mixture of RL settings. RLJ 3: 1072-1105 (2024) - [j6]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. Trans. Mach. Learn. Res. 2024 (2024) - [c37]Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. ICML 2024 - [c36]Johan Samir Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro:
In value-based deep reinforcement learning, a pruned network is a good network. ICML 2024 - [c35]Johan Samir Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro:
Mixtures of Experts Unlock Parameter Scaling for Deep RL. ICML 2024 - [c34]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:
Adaptive Accompaniment with ReaLchords. ICML 2024 - [i41]Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro:
Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR abs/2402.08609 (2024) - [i40]Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro:
In deep reinforcement learning, a pruned network is a good network. CoRR abs/2402.12479 (2024) - [i39]Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal:
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. CoRR abs/2403.03950 (2024) - [i38]Johan S. Obando-Ceron, João G. M. Araújo, Aaron C. Courville, Pablo Samuel Castro:
On the consistency of hyper-parameter selection in value-based deep reinforcement learning. CoRR abs/2406.17523 (2024) - [i37]Timon Willi, Johan S. Obando-Ceron, Jakob N. Foerster, Karolina Dziugaite, Pablo Samuel Castro:
Mixture of Experts in a Mixture of RL settings. CoRR abs/2406.18420 (2024) - [i36]Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange, Chris Lu, Pablo Samuel Castro, Laura Toni:
NAVIX: Scaling MiniGrid Environments with JAX. CoRR abs/2407.19396 (2024) - [i35]Ghada Sokar, Johan S. Obando-Ceron, Aaron C. Courville, Hugo Larochelle, Pablo Samuel Castro:
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL. CoRR abs/2410.01930 (2024) - [i34]Jesse Farebrother, Pablo Samuel Castro:
CALE: Continuous Arcade Learning Environment. CoRR abs/2410.23810 (2024) - 2023
- [j5]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. Trans. Mach. Learn. Res. 2023 (2023) - [c33]Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare:
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. ICLR 2023 - [c32]Johan Samir Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro:
The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. Tiny Papers @ ICLR 2023 - [c31]Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. ICML 2023: 30365-30380 - [c30]Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci:
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. ICML 2023: 32145-32168 - [c29]Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo Perez-Vicente, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan K. Terry:
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks. NeurIPS 2023 - [c28]Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro:
Small batch deep reinforcement learning. NeurIPS 2023 - [c27]Laixi Shi
, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist:
Offline Reinforcement Learning with On-Policy Q-Function Regularization. ECML/PKDD (4) 2023: 455-471 - [i33]Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci:
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. CoRR abs/2302.12902 (2023) - [i32]Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare:
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. CoRR abs/2304.12567 (2023) - [i31]Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci:
JaxPruner: A concise library for sparsity research. CoRR abs/2304.14082 (2023) - [i30]Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro:
Bigger, Better, Faster: Human-level Atari with human-level efficiency. CoRR abs/2305.19452 (2023) - [i29]Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan K. Terry:
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks. CoRR abs/2306.13831 (2023) - [i28]Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist:
Offline Reinforcement Learning with On-Policy Q-Function Regularization. CoRR abs/2307.13824 (2023) - [i27]Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro:
Small batch deep reinforcement learning. CoRR abs/2310.03882 (2023) - [i26]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. CoRR abs/2310.19804 (2023) - [i25]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. CoRR abs/2311.14115 (2023) - [i24]Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore:
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR abs/2311.17894 (2023) - 2022
- [c26]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS 2022: 8619-8649 - [c25]Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro:
The State of Sparse Training in Deep Reinforcement Learning. ICML 2022: 7766-7792 - [c24]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS 2022 - [i23]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR abs/2206.01626 (2022) - [i22]Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro:
The State of Sparse Training in Deep Reinforcement Learning. CoRR abs/2206.10369 (2022) - 2021
- [c23]Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro:
Metrics and Continuity in Reinforcement Learning. AAAI 2021: 8261-8269 - [c22]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR 2021 - [c21]Johan Samir Obando-Ceron, Pablo Samuel Castro:
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. ICML 2021: 1373-1383 - [c20]Georg Ostrovski, Pablo Samuel Castro, Will Dabney:
The Difficulty of Passive Learning in Deep Reinforcement Learning. NeurIPS 2021: 23283-23295 - [c19]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. NeurIPS 2021: 29304-29320 - [c18]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
MICo: Improved representations via sampling-based state similarity for Markov decision processes. NeurIPS 2021: 30113-30126 - [i21]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR abs/2101.05265 (2021) - [i20]Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro:
Metrics and continuity in reinforcement learning. CoRR abs/2102.01514 (2021) - [i19]Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland:
MICo: Learning improved representations via sampling-based state similarity for Markov decision processes. CoRR abs/2106.08229 (2021) - [i18]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A functional mirror ascent view of policy gradient methods with function approximation. CoRR abs/2108.05828 (2021) - [i17]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR abs/2108.13264 (2021) - [i16]Georg Ostrovski, Pablo Samuel Castro, Will Dabney:
The Difficulty of Passive Learning in Deep Reinforcement Learning. CoRR abs/2110.14020 (2021) - [i15]Pablo Samuel Castro:
Losses, Dissonances, and Distortions. CoRR abs/2111.05128 (2021) - 2020
- [j4]Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda, Ziyu Wang:
Autonomous navigation of stratospheric balloons using reinforcement learning. Nat. 588(7836): 77-82 (2020) - [c17]Pablo Samuel Castro:
Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes. AAAI 2020: 10069-10076 - [c16]Kory Wallace Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare:
Shaping the Narrative Arc: Information-Theoretic Collaborative DialoguePaper type: Technical Paper. ICCC 2020: 9-16 - [c15]Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen:
Rigging the Lottery: Making All Tickets Winners. ICML 2020: 2943-2952 - [i14]Pablo Samuel Castro:
GANterpretations. CoRR abs/2011.05158 (2020) - [i13]Johan S. Obando-Ceron, Pablo Samuel Castro:
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. CoRR abs/2011.14826 (2020)
2010 – 2019
- 2019
- [c14]Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro:
A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI 2019: 4504-4511 - [c13]Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. AISTATS 2019: 2203-2211 - [c12]Pablo Samuel Castro:
Performing Structured Improvisations with Pre-trained Deep Learning Models. ICCC 2019: 306-310 - [c11]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:
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. IJCAI 2019: 3260-3267 - [c10]Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS 2019: 4360-4371 - [i12]Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare:
A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR abs/1901.11084 (2019) - [i11]Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare:
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue. CoRR abs/1901.11528 (2019) - [i10]Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR abs/1901.11530 (2019) - [i9]Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. CoRR abs/1902.03149 (2019) - [i8]Pablo Samuel Castro:
Performing Structured Improvisations with pre-trained Deep Learning Models. CoRR abs/1904.13285 (2019) - [i7]Pablo Samuel Castro, Shijian Li, Daqing Zhang:
Inverse Reinforcement Learning with Multiple Ranked Experts. CoRR abs/1907.13411 (2019) - [i6]Pablo Samuel Castro:
Scalable methods for computing state similarity in deterministic Markov Decision Processes. CoRR abs/1911.09291 (2019) - [i5]Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen:
Rigging the Lottery: Making All Tickets Winners. CoRR abs/1911.11134 (2019) - 2018
- [i4]Pablo Samuel Castro, Maria Attarian:
Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation. CoRR abs/1811.04651 (2018) - [i3]Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare:
Dopamine: A Research Framework for Deep Reinforcement Learning. CoRR abs/1812.06110 (2018) - [i2]Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman:
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. CoRR abs/1812.07069 (2018) - 2013
- [j3]Pablo Samuel Castro, Daqing Zhang, Chao Chen, Shijian Li, Gang Pan:
From taxi GPS traces to social and community dynamics: A survey. ACM Comput. Surv. 46(2): 17:1-17:34 (2013) - [j2]Lin Sun, Daqing Zhang, Chao Chen, Pablo Samuel Castro, Shijian Li, Zonghui Wang:
Real Time Anomalous Trajectory Detection and Analysis. Mob. Networks Appl. 18(3): 341-356 (2013) - [j1]Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, Shijian Li, Zonghui Wang:
iBOAT: Isolation-Based Online Anomalous Trajectory Detection. IEEE Trans. Intell. Transp. Syst. 14(2): 806-818 (2013) - 2012
- [c9]Pablo Samuel Castro, Daqing Zhang, Shijian Li:
Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces. Pervasive 2012: 57-72 - [i1]Norman Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden:
Methods for computing state similarity in Markov Decision Processes. CoRR abs/1206.6836 (2012) - 2011
- [c8]Pablo Samuel Castro, Doina Precup:
Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics. EWRL 2011: 140-152 - [c7]Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, Shijian Li:
Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces. MobiQuitous 2011: 63-74 - 2010
- [c6]Pablo Samuel Castro, Doina Precup:
Using Bisimulation for Policy Transfer in MDPs. AAAI 2010: 1065-1070 - [c5]Pablo Samuel Castro, Doina Precup:
Using bisimulation for policy transfer in MDPs. AAMAS 2010: 1399-1400 - [c4]Pablo Samuel Castro, Doina Precup:
Smarter Sampling in Model-Based Bayesian Reinforcement Learning. ECML/PKDD (1) 2010: 200-214
2000 – 2009
- 2009
- [c3]Pablo Samuel Castro, Prakash Panangaden, Doina Precup:
Equivalence Relations in Fully and Partially Observable Markov Decision Processes. IJCAI 2009: 1653-1658 - 2007
- [c2]Pablo Samuel Castro, Doina Precup:
Using Linear Programming for Bayesian Exploration in Markov Decision Processes. IJCAI 2007: 2437-2442 - 2006
- [c1]Norm Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden:
Methods for Computing State Similarity in Markov Decision Processes. UAI 2006
Coauthor Index
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last updated on 2024-12-01 00:17 CET by the dblp team
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