21
Jul
2023
ICML 2023: Over 80 Mila-affiliated research papers
From July 23 to July 29, 2023, Mila researchers will attend the Fortieth International Conference on Machine Learning (ICML) in Hawaii. They will share over 80 publications in oral presentations, poster sessions and workshops in front of other experts from all around the world.
Here is a list of ICML 2023 papers that contain at least one Mila-affiliated author :
Title |
Authors |
| Cognitive Models as Simulators: Using Cognitive Models to Tap into Implicit Human Feedback | Ardavan S. Nobandegani, Thomas Shultz, Irina Rish |
| FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning | Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Zhitao Ying, Jian Tang, Peilin Zhao, Dinghao Wu |
| Neural FIM for learning Fisher information metrics from point cloud data | Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian M. Adelstein, Smita Krishnaswamy |
| Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels | Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste |
| Lie Point Symmetry and Physics Informed Networks | Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh |
| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael Rabbat |
| Assessing Neural Network Representations During Training Using Data Diffusion Spectra | Danqi Liao, Chen Liu, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian M. Adelstein, Smita Krishnaswamy |
| Accelerating exploration and representation learning with offline pre-training | Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, Ankit Anand |
| Sampling-Based Accuracy Testing of Posterior Estimators for General Inference | Pablo Lemos, A. Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur |
| Bootstrapped Representations in Reinforcement Learning | Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney |
| Can Forward Gradient Match Backpropagation? | Louis Fournier, Stephane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon |
| High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance | A. Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horv’ath, Gauthier Gidel, P. Dvurechensky, A. Gasnikov, Peter Richtarik |
| Equivariance With Learned Canonicalization Functions | Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh |
| Repository-Level Prompt Generation for Large Language Models of Code | Disha Shrivastava, Hugo Larochelle, Danny Tarlow |
| Uncertain Evidence in Probabilistic Models and Stochastic Simulators | Andreas Munk, A. Mead, Frank Wood |
| Target-based Surrogates for Stochastic Optimization | J. Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Roux |
| Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection | Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent |
| ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts | Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang |
| Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts | E. Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados |
| Towards Reliable Neural Specifications | Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, A. Gurfinkel, Xujie Si |
| Better Training of GFlowNets with Local Credit and Incomplete Trajectories | L. Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio |
| Maximal Initial Learning Rates in Deep ReLU Networks | Gaurav Iyer, Boris Hanin, David Rolnick |
| Hidden Symmetries of ReLU Networks | J. Grigsby, Elisenda Grigsby, Kathryn A. Lindsey, David Rolnick |
| Bidirectional Learning for Offline Model-based Biological Sequence Design | Can (Sam) Chen, Yingxue Zhang, Xue Liu, Mark Coates |
| PAC-Bayesian Generalization Bounds for Adversarial Generative Models | Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain |
| Deep Networks as Paths on the Manifold of Neural Representations | Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad P. Kording |
| Discovering Object-Centric Generalized Value Functions From Pixels | Somjit Nath, G. Subbaraj, Khimya Khetarpal, Samira E. Kahou |
| Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity | Eduard Gorbunov, Adrien Taylor, Samuel Horv’ath, Gauthier Gidel |
| Mechanistic Mode Connectivity | E. S. Lubana, Eric J. Bigelow, Robert P. Dick, David Krueger, Hidenori Tanaka |
| Evolving Computation Graphs | Andreea Deac, Jian Tang |
| Flexible Phase Dynamics for Bio-Plausible Contrastive Learning | Ezekiel Williams, C. Bredenberg, Guillaume Lajoie |
| Hyena Hierarchy: Towards Larger Convolutional Language Models | Michael Poli, Stefano Massaroli, Eric Q. Nguyen, Daniel Y. Fu, Tri Dao, S. Baccus, Yoshua Bengio, S. Ermon, Christopher Re |
| Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? | Boris Knyazev, Doha Hwang, Simon Lacoste-Julien |
| Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization | Chris Junchi Li, An Yuan, Gauthier Gidel, Quanquan Gu, Michael Jordan |
| Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning | Nader Asadi, Mohammad-Javad Davari, S. Mudur, Rahaf Aljundi, Eugene Belilovsky |
| A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining | Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo, Jian Tang |
| GFlowNet-EM for learning compositional latent variable models | Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E Everett, Alexandros Graikos, Yoshua Bengio |
| Interventional Causal Representation Learning | Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio |
| Multi-Environment Pretraining Enables Transfer to Action Limited Datasets | David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum |
| Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design | Julien Roy, Pierre-luc Bacon, Christopher Joseph Pal, Emmanuel Bengio |
| Omega: Optimistic EMA Gradients | Juan Ramirez, Rohan Sukumaran, Quentin Bertrand, Gauthier Gidel |
| Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning | Sebastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand |
| GFlowOut: Dropout with Generative Flow Networks | Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal Alias Parth Goyal, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio |
| FAENet: Frame Averaging Equivariant GNN for Materials Modeling | Alexandre Duval, Victor Schmidt, Alex Hernandez-Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick |
| Learning GFlowNets From Partial Episodes For Improved Convergence And Stability | Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, A. Nica, Andrei Cristian Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin |
| The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation | Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney |
| 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 |
| A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction | Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian M. Adelstein, Smita Krishnaswamy |
| R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents | Daniel Dun-ning Woo Johnson, Danny Tarlow, Christian J. Walder |
| A theory of continuous generative flow networks | Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernandez-Garcia, L’ena N’ehale Ezzine, Yoshua Bengio, Nikolay Malkin |
| Graphically Structured Diffusion Models | Christian Weilbach, William Harvey, Frank Wood |
| CrossSplit: Mitigating Label Noise Memorization through Data Splitting | Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien |
| Graph Inductive Biases in Transformers without Message Passing | Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, P. Dokania, Mark Coates, Philip Torr, Ser-Nam Lim, S. Lim |
| Discrete Key-Value Bottleneck | Frederik Träuble, Anirudh Goyal Alias Parth Goyal, Anirudh Goyal, Nasim Rahaman, Michael C. Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Scholkopf |
| Multi-Objective GFlowNets | Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio |
| Unlocking Slot Attention by Changing Optimal Transport Costs | Yan Zhang, David W Zhang, Simon Lacoste-Julien, G. Burghouts, Cees G. M. Snoek |
| Joint Bayesian inference of graphical structure and parameters with a single generative flow network | Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio |
| BatchGFN: Generative flow networks for batch active learning | Shreshth Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal |
| Thompson sampling for improved exploration in GFlowNets | Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Chenghao Liu, Sarath Chandar, Nikolay Malkin, Yoshua Bengio |
| Improving and generalizing flow-based generative models with minibatch optimal transport | Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, Yoshua Bengio |
| Simulation-free Schrödinger bridges via score and flow matching | Alexander Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Yoshua Bengio |
| Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks | David W Zhang, Miltiadis Kofinas, Yan Zhang, Yunlu Chen, Gertjan J. Burghouts, Cees G. M. Snoek |
| Maximum State Entropy Exploration using Predecessor and Successor Representations | Arnav Kumar Jain, Lucas Lehnert, Irina Rish, Glen Berseth |
| SimBIG: Field-level Simulation-based Inference of Large-scale Structure | Pablo Lemos, Liam Parker, ChangHoon Hahn, Bruno Régaldo-Saint Blancard, Elena Massara, Shirley Ho, David Spergel, Chirag Modi, Azadeh Moradinezhad Dizgah, Michael Eickenberg, Jiamin Hou |
| SimBIG: Galaxy Clustering beyond the Power Spectrum | ChangHoon Hahn, Pablo Lemos, Bruno Régaldo-Saint Blancard, Liam Parker, Michael Eickenberg, Shirley Ho, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, David Spergel |
| Deep Laplacian-based Options for Temporally-Extended Exploration | Martin Klissarov and Marlos C. Machado |
| Time Delay Cosmography with a Neural Ratio Estimator | Eve Campeau-Poirier, Laurence Perreault-Levasseur, Adam Coogan, Yashar Hezaveh |
| Towards Unbiased Gravitational-Wave Parameter Estimation using Score-Based Likelihood Characterization | Ronan Legin, Kaze Wong, Maximiliano Isi, Alexandre Adam, Laurence Perreault-Levasseur, Yashar Hezaveh |
| Diffusion Based Representation Learning | Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Scholkopf, Arash Mehrjou |
| Adversarial Policies Beat Superhuman Go AIs | Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell |
| Do as your neighbors: Invariant learning through non-parametric neighbourhood matching | Andrei Liviu Nicolicioiu, Jerry Huang, Dhanya Sridhar, Aaron Courville |
| Learning Diverse Features in Vision Transformers for Improved Generalization | Armand Mihai Nicolicioiu, Andrei Liviu Nicolicioiu, Bogdan Alexe, Damien Teney |
| Towards Out-of-Distribution Adversarial Robustness | Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan |
| Continual Pre-Training of Large Language Models: How to re-warm your model? | Kshitij Gupta*, Benjamin Thérien*, Adam Ibrahim*, Mats Leon Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish |
| Idiolect: A Reconfigurable Voice Coding Assistant | Breandan Considine, Nicholas Albion, Xujie Si |
| ROSA: Random Orthogonal Subspace Adaptation | Marawan Gamal, Guillaume Rabusseau |
| GFlowNets for Causal Discovery: an Overview | Cristian Dragos Manta, Edward J. Hu, Yoshua Bengio |
| What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context? | Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio |
| Guiding The Last Layer in Federated Learning with Pre-Trained Models | Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky |
| Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning | Gwen Legate, Lucas Caccia, Eugene Belilovsky |
| Learning to Optimize with Recurrent Hierarchical Transformers | Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky |
| Abstracting Imperfect Information Away from Two-Player Zero-Sum Games | Samuel Sokota, Ryan D’Orazio, Chun Kai Ling, David Wu, Zico Kolter, Noam Brown |