News
Awards
Rishika Bhagwatkar's study "Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques" has been selected as one of the Outstanding papers at the ICML TiFA Workshop 2024
Kartik Ahuja received an IVADO scholarship.
Irene Tenison received a Microsoft Diversity Award scholarship.
Sreya Francis received a Microsoft Research Diversity Award scholarship for 2021.
Amin Mansouri received 2 scholarships from both Faculty of Art & Sciences (FAS, University of Montreal) and
Microsoft Diversity Award.
Shanel Gauthier received 2 scholarships from both Faculty of Art & Sciences (FAS-AI, University of Montreal)
and NSERC.
June 2021
Kartik Ahuja collaborated on the following paper which has been accepted to ICML 2021:
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? ( https://arxiv.org/abs/2106.02890 )
Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh, Mojtaba Faramarzi, Kartik Ahuja, and Irina Rish collaborated on the following paper:
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization ( https://arxiv.org/abs/2106.02266 )
Timothée Lesort, Thomas George, and Irina Rish collaborated on the following publication:
Continual Learning in Deep Networks: an Analysis of the Last Layer ( https://arxiv.org/abs/2106.01834 )
Matthew Riemer has collaborated on the following paper which has been accepted to ICML 2021:
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning ( https://arxiv.org/abs/2011.00382)
The following members have joined the team:
Lucas Lehnert joined as a PostDoc
Guillaume Lam and Lucas Cecchi joined as Interns
Aryan Pandalai and Mark Takken joined as High School Students
May 2021
Matthew Riemer collaborated on the following publication:
Coagent Networks Revisited ( https://arxiv.org/abs/2001.10474 )
April 2021
Irene Tenison, Sreya Francis, and Irina Rish collaborated on the following two publications, which were present in the ICLR 2021 Distributed and Private Machine Learning(DPML) Workshop:
Gradient Masked Federated Optimization ( https://arxiv.org/abs/2104.10322 )
Towards Causal Federated Learning For Enhanced Robustness and Privacy ( https://arxiv.org/abs/2104.06557 )
Timothée Lesort, Massimo Caccia, and Irina Rish collaborated on the following publication:
Understanding Continual Learning Settings with Data Distribution Drift Analysis ( https://arxiv.org/abs/2104.01678 )
Timothée Lesort collaborated on the second version of the following publication:
Regularization Shortcomings for Continual Learning ( https://arxiv.org/abs/1912.03049 )
March 2021
Kartik Ahuja collaborated in the following publication :
Treatment Effect Estimation using Invariant Risk Minimization ( https://arxiv.org/abs/2103.07788 )