Here is a schedule featuring Mila-affiliated researchers presenting their work at ICLR 2024. All times are in Central European Time (CET).
A PDF version is also available here
Mila @ ICLR 2024, 07.05.2024
Poster Session 1 - 10.45 a.m. (CET)
#13 Evaluating Representation Learning on the Protein Structure Universe : Arian Rokkum Jamasb, Alex Morehead, Zuobai Zhang, Chaitanya K. Joshi, Kieran Didi, Simon V Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom Leon Blundell
#49 Synaptic Weight Distributions Depend on the Geometry of Plasticity : Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel, Guillaume Lajoie, Blake Aaron Richards
#133 The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning : Tian Jin, Nolan Clement, X. Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
#266 LOQA: Learning with Opponent Q-Learning Awareness : Milad Aghajohari, Juan Agustin Duque, Tim Cooijmans, Aaron Courville
Orals - 3.45 p.m. (CET)
Oral 2A
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions : Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade
Oral 2B
Ghost on the Shell: An Expressive Representation of General 3D Shapes : Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Scholkopf
Oral 2C
Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models :
Pablo Pernias, Dominic Rampas, Mats L. Richter, Christopher Joseph Pal, Marc Aubreville
Poster Session 2 - 4.30 p.m. (CET)
#72 On the Stability of Iterative Retraining of Generative Models on their own Data : Quentin Bertrand, Avishek Joey Bose, Alexandre Duplessis, Marco Jiralerspong, Gauthier Gidel
#79 Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models : Pablo Pernias, Dominic Rampas, Mats L. Richter, Christopher Joseph Pal, Marc Aubreville
#157 Course Correcting Koopman Representations : Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin
#160 Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions : Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade
#217 Object-centric architectures enable efficient causal representation learning : Amin Mansouri, Jason Hartford, Yan Zhang, Yoshua Bengio
#279 Ghost on the Shell: An Expressive Representation of General 3D Shapes : Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Scholkopf
#284 Poly-View Contrastive Learning : Amitis Shidani, Dan Busbridge, Devon Hjelm, Jason Ramapuram, Eeshan Gunesh Dhekane, Russell Webb
#290 On Diffusion Modeling for Anomaly Detection : Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh
Mila @ ICLR 2024, 08.05.2024
Poster Session 3 - 10.45 a.m. (CET)
#5 Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets : Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, S. Maddrell-Mander, Callum McLean, Frederick Wenkel, Luis Müller, Jama Hussein Mohamud, Alipanah Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Ioannis Koutis, Christopher Morris, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Thérence Bois, A. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters
#7 Searching for High-Value Molecules Using Reinforcement Learning and Transformers : Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen Berseth
#41 Tree Cross Attention : Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Yoshua Bengio, M. O. Ahmed
#53 Sufficient conditions for offline reactivation in recurrent neural networks : Nanda H Krishna, Colin Bredenberg, Daniel Levenstein, Blake Aaron Richards, Guillaume Lajoie
#60 Ensemble Distillation for Unsupervised Constituency Parsing : Behzad Shayegh, Yanshuai Cao, Xiaodan Zhu, Jackie CK Cheung, Lili Mou
#98 GraphPulse: Topological representations for temporal graph property prediction : Kiarash Shamsi, Farimah Poursafaei, Shenyang Huang, Bao Tran Gia Ngo, Baris Coskunuzer, Cuneyt Gurcan Akcora
#138 Towards Foundation Models for Knowledge Graph Reasoning : Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, Zhaocheng Zhu
#177 Local Search GFlowNets : Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
#181 Improving Intrinsic Exploration by Creating Stationary Objectives : Roger Creus Castanyer, Joshua Romoff, Glen Berseth
#214 Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization : Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio
#265 GOAt: Explaining Graph Neural Networks via Graph Output Attribution : Shengyao Lu, Keith G Mills, Jiao He, Bang Liu, Di Niu
#307 ReFusion : Improving Natural Language Understanding with Computation-Efficient Retrieval Augmentation : Shangyu Wu, Ying Xiong, Yufei CUI, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue
Orals - 3.45 p.m. (CET)
Oral 4D
Amortizing intractable inference in large language models : Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin
Poster Session 4 - 4.30 p.m. (CET)
#60 How connectivity structure shapes rich and lazy learning in neural circuits : Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie
#71 Pre-Training and Fine-Tuning Generative Flow Networks : L. Pan, Moksh Jain, Kanika Madan, Yoshua Bengio
#93 Amortizing intractable inference in large language models : Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin
#192 Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation : Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
#198 Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View : Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach
#221 Balancing Act: Sparse Models with Constrained Disparate Impact : Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran, Golnoosh Farnadi, Simon Lacoste-Julien, Jose Gallego-Posada
#228 Mechanistic analysis of procedurally generated tasks : Samyak Jain, Robert Kirk, E. S. Lubana, Robert P. Dick, Hidenori Tanaka, Tim Rocktäschel, Edward Grefenstette, David Krueger
Mila @ ICLR 2024, 09.05.2024
Poster Session 5 - 10.45 a.m. (CET)
#73 SE(3)-Stochastic Flow Matching for Protein Backbone Generation : Avishek Joey Bose*, Tara Akhound-Sadegh*, Guillaume Huguet, Kilian FATRAS, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong
#125 Cycle Consistency Driven Object Discovery : Aniket Didolkar, Anirudh Goyal, Yoshua Bengio
#145 TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series : Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin
#171 Learning Multi-Agent Communication with Contrastive Learning : Yat Long Lo, Biswa Sengupta, Jakob Foerster, Michael Noukhovitch
Poster Session 6 - 4.30 p.m. (CET)
#77 Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency : Tianhong Li, Sangnie Bhardwaj, Yonglong Tian, Han Zhang, Jarred Barber, Dina Katabi, Guillaume Lajoie, Huiwen Chang, Dilip Krishnan
#142 Efficient Dynamics Modeling in Interactive Environments with Koopman Theory : Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, K. Siddiqi, Siamak Ravanbakhsh
#157 Reasoning with Latent Diffusion in Offline Reinforcement Learning : Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth
#160 Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo : Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
#164 Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning : Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio
#169 Delta-AI: Local objectives for amortized inference in sparse graphical models : Jean-Pierre R. Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
Mila @ ICLR 2024, 10.05.2024
Poster Session 7 - 10.45 a.m. (CET)
#109 Decoupling regularization from the action space : Sobhan Mohammadpour, Emma Frejinger, Pierre-luc Bacon
#149 Bridging State and History Representations: Understanding Self-Predictive RL : Tianwei Ni, Benjamin Eysenbach, Erfan SeyedSalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-luc Bacon
#155 Intelligent Switching for Reset-Free RL : Darshan Patil, Janarthanan Rajendran, Glen Berseth, Sarath Chandar
#203 Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation : Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Boyu Wang, Jun Yan, Xue Liu
#264 Large Language Models as Generalizable Policies for Embodied Tasks : Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Rin Metcalf, Katherine Metcalf, Walter Talbott, Natalie Mackraz, Devon Hjelm, Alexander Toshev
Motif: Intrinsic Motivation from Artificial Intelligence Feedback : Martin Klissarov, P. D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Orals - 3.45 p.m. (CET)
Oral 8C
Mastering Memory Tasks with World Models : Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar
Poster Session 8 - 4.30 p.m. (CET)
#5 Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling : Jiarui Lu, Bozitao Zhong, Zuobai Zhang, Jian Tang
#107 The Curse of Diversity in Ensemble-Based Exploration : Zhixuan Lin, P. D'Oro, Evgenii Nikishin, Aaron Courville
#149 Expected flow networks in stochastic environments and two-player zero-sum games : Marco Jiralerspong, Bilun Sun, Danilo Vucetic, Tianyu Zhang, Yoshua Bengio, Gauthier Gidel, Nikolay Malkin
#183 Mastering Memory Tasks with World Models : Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar
#191 Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning : Maxime Wabartha, Joelle Pineau
