Courville, Aaron

103 publications

ICLR 2025 Advantage Alignment Algorithms Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Razvan Ciuca, Tianyu Zhang, Gauthier Gidel, Aaron Courville

ICLR 2025 Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron Courville

NeurIPS 2025 Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models Samuel Lavoie, Michael Noukhovitch, Aaron Courville

ICLR 2025 Don't Flatten, Tokenize! Unlocking the Key to SoftMoE's Efficacy in Deep RL Ghada Sokar, Johan Samir Obando Ceron, Aaron Courville, Hugo Larochelle, Pablo Samuel Castro

ICML 2025 FLAM: Frame-Wise Language-Audio Modeling Yusong Wu, Christos Tsirigotis, Ke Chen, Cheng-Zhi Anna Huang, Aaron Courville, Oriol Nieto, Prem Seetharaman, Justin Salamon

ICLR 2025 Forgetting Transformer: SoftMax Attention with a Forget Gate Zhixuan Lin, Evgenii Nikishin, Xu He, Aaron Courville

NeurIPS 2025 Measure Gradients, Not Activations! Enhancing Neuronal Activity in Deep Reinforcement Learning Jiashun Liu, Zihao Wu, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Ling Pan

ICML 2025 Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Hongyao Tang, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Glen Berseth

NeurIPS 2025 Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation Sangmin Bae, Yujin Kim, Reza Bayat, Sungnyun Kim, Jiyoun Ha, Tal Schuster, Adam Fisch, Hrayr Harutyunyan, Ziwei Ji, Aaron Courville, Se-Young Yun

ICLR 2025 Neuroplastic Expansion in Deep Reinforcement Learning Jiashun Liu, Johan Samir Obando Ceron, Aaron Courville, Ling Pan

ICLR 2025 Scaling Stick-Breaking Attention: An Efficient Implementation and In-Depth Study Shawn Tan, Songlin Yang, Aaron Courville, Rameswar Panda, Yikang Shen

NeurIPS 2025 Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning Roger Creus Castanyer, Johan Obando-Ceron, Lu Li, Pierre-Luc Bacon, Glen Berseth, Aaron Courville, Pablo Samuel Castro

ICML 2025 The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning Jiashun Liu, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Ling Pan

ICML 2025 The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Walter Mayor, Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro

ICML 2025 VinePPO: Refining Credit Assignment in RL Training of LLMs Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, Nicolas Le Roux

ICML 2024 Adaptive Accompaniment with ReaLchords Yusong Wu, Tim Cooijmans, Kyle Kastner, Adam Roberts, Ian Simon, Alexander Scarlatos, Chris Donahue, Cassie Tarakajian, Shayegan Omidshafiei, Aaron Courville, Pablo Samuel Castro, Natasha Jaques, Cheng-Zhi Anna Huang

ICMLW 2024 Advantage Alignment Algorithms Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Tianyu Zhang, Aaron Courville

ICLR 2024 Diffusion Generative Flow Samplers: Improving Learning Signals Through Partial Trajectory Optimization Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio

TMLR 2024 Distributional GFlowNets with Quantile Flows Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

NeurIPSW 2024 Faster, More Efficient RLHF Through Off-Policy Asynchronous Learning Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron Courville

NeurIPS 2024 GenRL: Multimodal-Foundation World Models for Generalization in Embodied Agents Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron Courville, Sai Rajeswar

ICML 2024 In Value-Based Deep Reinforcement Learning, a Pruned Network Is a Good Network Johan Samir Obando Ceron, Aaron Courville, Pablo Samuel Castro

ICLR 2024 LOQA: Learning with Opponent Q-Learning Awareness Milad Aghajohari, Juan Agustin Duque, Tim Cooijmans, Aaron Courville

ICML 2024 Modeling Caption Diversity in Contrastive Vision-Language Pretraining Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mido Assran, Andrew Gordon Wilson, Aaron Courville, Nicolas Ballas

ICMLW 2024 Multimodal Foundation World Models for Generalist Embodied Agents Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron Courville, Sai Rajeswar

NeurIPSW 2024 Not All LLM Reasoners Are Created Equal Arian Hosseini, Alessandro Sordoni, Daniel Kenji Toyama, Aaron Courville, Rishabh Agarwal

NeurIPSW 2024 Not All LLM Reasoners Are Created Equal Arian Hosseini, Alessandro Sordoni, Daniel Kenji Toyama, Aaron Courville, Rishabh Agarwal

ECCV 2024 SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning Bac Nguyen, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, Aaron Courville

ECCV 2024 SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision Ankit Vani, Bac Nguyen, Samuel Lavoie, Ranjay Krishna, Aaron Courville

ICLR 2024 The Curse of Diversity in Ensemble-Based Exploration Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron Courville

NeurIPSW 2024 VinePPO: Accurate Credit Assignment in RL for LLM Mathematical Reasoning Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, Nicolas Le Roux

ICML 2023 Bigger, Better, Faster: Human-Level Atari with Human-Level Efficiency Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, Pablo Samuel Castro

ICLR 2023 Generative Augmented Flow Networks Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio

ICLR 2023 Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

ICLR 2023 Latent State Marginalization as a Low-Cost Approach for Improving Exploration Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

NeurIPSW 2023 Learning Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy Max Schwarzer, Jesse Farebrother, Joshua Greaves, Kevin Roccapriore, Ekin Cubuk, Rishabh Agarwal, Aaron Courville, Marc Bellemare, Sergei Kalinin, Igor Mordatch, Pablo Castro

ICMLW 2023 Learning with Learning Awareness Using Meta-Values Tim Cooijmans, Milad Aghajohari, Aaron Courville

ICML 2023 Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste

ICLR 2023 Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville

ICLR 2023 Simplicial Embeddings in Self-Supervised Learning and Downstream Classification Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, Aaron Courville

TMLR 2023 Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron Courville, Alessandro Sordoni

ICML 2022 Building Robust Ensembles via Margin Boosting Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala

ICLR 2022 Chunked Autoregressive GAN for Conditional Waveform Synthesis Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron Courville, Yoshua Bengio

ICLR 2022 DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine

ICLR 2022 Fortuitous Forgetting in Connectionist Networks Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville

ICML 2022 Generative Flow Networks for Discrete Probabilistic Modeling Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio

ICLRW 2022 INFERNO: Inferring Object-Centric 3D Scene Representations Without Supervision Lluis Castrejon, Nicolas Ballas, Aaron Courville

ICLRW 2022 Introducing Coordination in Concurrent Reinforcement Learning Adrien Ali Taiga, Aaron Courville, Marc G Bellemare

NeurIPSW 2022 Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

ICLR 2022 Learning to Dequantise with Truncated Flows Shawn Tan, Chin-Wei Huang, Alessandro Sordoni, Aaron Courville

ICLR 2022 MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Tim Cooijmans, Aaron Courville, Cheng-Zhi Anna Huang, Jesse Engel

CVPR 2022 Multi-Label Iterated Learning for Image Classification with Label Ambiguity Sai Rajeswar, Pau Rodríguez, Soumye Singhal, David Vazquez, Aaron Courville

NeurIPSW 2022 Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville

ICML 2022 The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville

ICLR 2022 Unifying Likelihood-Free Inference with Black-Box Optimization and Beyond Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

NeurIPSW 2022 Unleashing the Potential of Data Sharing in Ensemble Deep Reinforcement Learning Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron Courville

NeurIPSW 2022 Unleashing the Potential of Data Sharing in Ensemble Deep Reinforcement Learning Zhixuan Lin, Pierluca D'Oro, Evgenii Nikishin, Aaron Courville

ICMLW 2022 Unsupervised Model-Based Pre-Training for Data-Efficient Reinforcement Learning from Pixels Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste

CLeaR 2022 VIM: Variational Independent Modules for Video Prediction Rim Assouel, Lluis Castrejon, Aaron Courville, Nicolas Ballas, Yoshua Bengio

ICMLW 2021 A Variational Perspective on Diffusion-Based Generative Models and Score Matching Chin-Wei Huang, Jae Hyun Lim, Aaron Courville

NeurIPSW 2021 Behavior Predictive Representations for Generalization in Reinforcement Learning Siddhant Agarwal, Aaron Courville, Rishabh Agarwal

ICML 2021 Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

ICML 2021 Continuous Coordination as a Realistic Scenario for Lifelong Learning Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath Chandar

ICLR 2021 Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville

NeurIPSW 2021 DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine

ICLR 2021 Data-Efficient Reinforcement Learning with Self-Predictive Representations Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, Philip Bachman

ICCV 2021 Generative Compositional Augmentations for Scene Graph Prediction Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky

CoRL 2021 Haptics-Based Curiosity for Sparse-Reward Tasks Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro

ICLR 2021 Integrating Categorical Semantics into Unsupervised Domain Translation Samuel Lavoie-Marchildon, Faruk Ahmed, Aaron Courville

ICLR 2021 Iterated Learning for Emergent Systematicity in VQA Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

ICLR 2021 Learning Task Decomposition with Ordered Memory Policy Network Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

ICLR 2021 Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

ICML 2021 Out-of-Distribution Generalization via Risk Extrapolation (REx) David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville

ICLR 2021 Systematic Generalisation with Group Invariant Predictions Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron Courville

ICML 2020 AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang

ICML 2020 Countering Language Drift with Seeded Iterated Learning Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, Olivier Pietquin

ICLR 2020 On Bonus Based Exploration Methods in the Arcade Learning Environment Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

ICLRW 2020 Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models Chin-Wei Huang, Laurent Dinh, Aaron Courville

AISTATS 2020 Stochastic Neural Network with Kronecker Flow Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

ICML 2019 Hierarchical Importance Weighted Autoencoders Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville

ICML 2019 On the Spectral Bias of Neural Networks Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, Aaron Courville

ICLR 2019 Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville

UAI 2019 Probability Distillation: A Caveat and Alternatives Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville

ICLR 2019 Systematic Generalization: What Is Required and Can It Be Learned? Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville

ICML 2018 Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data Amjad Almahairi, Sai Rajeshwar, Alessandro Sordoni, Philip Bachman, Aaron Courville

Distill 2018 Feature-Wise Transformations Vincent Dumoulin, Ethan Perez, Nathan Schucher, Florian Strub, Harm de Vries, Aaron Courville, Yoshua Bengio

ICML 2018 Mutual Information Neural Estimation Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, Devon Hjelm

ICML 2018 Neural Autoregressive Flows Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

ICLR 2018 Neural Language Modeling by Jointly Learning Syntax and Lexicon Yikang Shen, Zhouhan Lin, Chin-wei Huang, Aaron Courville

ICML 2017 A Closer Look at Memorization in Deep Networks Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, Simon Lacoste-Julien

CVPR 2017 A Dataset and Exploration of Models for Understanding Video Data Through Fill-in-the-Blank Question-Answering Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, Christopher Pal

CVPR 2017 GuessWhat?! Visual Object Discovery Through Multi-Modal Dialogue Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron Courville

ICML 2016 Deconstructing the Ladder Network Architecture Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio

ICML 2016 Dynamic Capacity Networks Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville

ICCV 2015 Describing Videos by Exploiting Temporal Structure Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville

ICML 2015 Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio

NeurIPS 2014 Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

ICML 2013 Maxout Networks Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio

NeurIPS 2013 Multi-Prediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio

AISTATS 2011 A Spike and Slab Restricted Boltzmann Machine Aaron Courville, James Bergstra, Yoshua Bengio

AISTATS 2010 Tempered Markov Chain Monte Carlo for Training of Restricted Boltzmann Machines Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau

JMLR 2010 Why Does Unsupervised Pre-Training Help Deep Learning? Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio

AISTATS 2010 Why Does Unsupervised Pre-Training Help Deep Learning? Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent