Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data. - Johan S. Obando-Ceron; Victor Romero-Cano; Walter Mayor Toro (CoRR, 2019)

Divide and Conquer: an Accurate Machine Learning Algorithm to Process Split Videos on a Parallel Processing Infrastructure. - Walter M. Mayor Toro; Juan Carlos Perafan Villota; Oscar H. Mondragon; Johan S. Obando-Ceron (CoRR, 2019)

Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. - Johan S. Obando-Ceron; Pablo Samuel Castro (ICML, 2021)

The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. - Johan S. Obando-Ceron; Marc G. Bellemare; Pablo Samuel Castro (Tiny Papers @ ICLR, 2023)

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 (ICML, 2023)

Small batch deep reinforcement learning. - Johan S. Obando-Ceron; Marc G. Bellemare; Pablo Samuel Castro (NeurIPS, 2023)

Probabilistic Multimodal Depth Estimation Based on Camera-LiDAR Sensor Fusion. - Johan S. Obando-Ceron; Victor Romero-Cano; Sildomar Monteiro (CoRR, 2023)

Jaxpruner: A Concise Library for Sparsity Research. - Joo Hyung Lee; Wonpyo Park; Nicole Mitchell; Jonathan Pilault; Johan Samir Obando Ceron; Han-Byul Kim; Namhoon Lee; Elias Frantar; Yun Long; Amir Yazdanbakhsh; Woohyun Han; Shivani Agrawal; et al. (CPAL, 2024)

In value-based deep reinforcement learning, a pruned network is a good network. - Johan S. Obando-Ceron; Aaron C. Courville; Pablo Samuel Castro (ICML, 2024)

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 (ICML, 2024)

On the consistency of hyper-parameter selection in value-based deep reinforcement learning. - Johan S. Obando-Ceron; João Guilherme Madeira Araújo; Aaron C. Courville; Pablo Samuel Castro (RLJ, 2024)

Mixture of Experts in a Mixture of RL settings. - Timon Willi; Johan S. Obando-Ceron; Jakob Nicolaus Foerster; Gintare Karolina Dziugaite; Pablo Samuel Castro (RLJ, 2024)

In deep reinforcement learning, a pruned network is a good network. - Johan S. Obando-Ceron; Aaron C. Courville; Pablo Samuel Castro (CoRR, 2024)

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages. - Ashmal Vayani; Dinura Dissanayake; Hasindri Watawana; Noor Ahsan; Nevasini Sasikumar; Omkar Thawakar; Henok Biadglign Ademtew; Yahya Hmaiti; Amandeep Kumar; Kartik Kuckreja; Mykola Maslych; Wafa Al Ghallabi; et al. (CoRR, 2024)

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages. - Ashmal Vayani; Dinura Dissanayake; Hasindri Watawana; Noor Ahsan; Nevasini Sasikumar; Omkar Thawakar; Henok Biadglign Ademtew; Yahya Hmaiti; Amandeep Kumar; Kartik Kuckreja; Mykola Maslych; Wafa Al Ghallabi; et al. (CVPR, 2025)

Neuroplastic Expansion in Deep Reinforcement Learning. - Jiashun Liu; Johan S. Obando-Ceron; Aaron C. Courville; Ling Pan (ICLR, 2025)

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge. - Angelika Romanou; Negar Foroutan; Anna Sotnikova; Zeming Chen 001; Sree Harsha Nelaturu; Shivalika Singh; Rishabh Maheshwary; Micol Altomare; Mohamed A. Haggag; Imanol Schlag; Marzieh Fadaee; Sara Hooker; et al. (ICLR, 2025)

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 (ICLR, 2025)

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training. - Brian R. Bartoldson; Siddarth Venkatraman; James Diffenderfer; Moksh Jain; Tal Ben-Nun; Seanie Lee; Minsu Kim 004; Johan S. Obando-Ceron; Yoshua Bengio; Bhavya Kailkhura (CoRR, 2025)

Adaptive Computation Pruning for the Forgetting Transformer. - Zhixuan Lin; Johan S. Obando-Ceron; Xu Owen He; Aaron C. Courville (CoRR, 2025)

Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation. - Israfel Salazar; Manuel Fernández Burda; Shayekh Bin Islam; Arshia Soltani Moakhar; Shivalika Singh; Fabian Farestam; Angelika Romanou; Danylo Boiko; Dipika Khullar; Mike Zhang; Dominik Krzeminski; Jekaterina Novikova; et al. (CoRR, 2025)

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning. - Jiashun Liu; Zihao Wu; Johan S. Obando-Ceron; Pablo Samuel Castro; Aaron C. Courville; Ling Pan (CoRR, 2025)

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn. - Hongyao Tang; Johan S. Obando-Ceron; Pablo Samuel Castro; Aaron C. Courville; Glen Berseth (CoRR, 2025)

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 (CoRR, 2025)

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 (CoRR, 2025)

Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning. - Roger Creus Castanyer; Johan S. Obando-Ceron; Lu Li; Pierre-Luc Bacon; Glen Berseth; Aaron C. Courville; Pablo Samuel Castro (CoRR, 2025)