Awards

• 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 )