Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. - Kartik Ahuja; Ethan Caballero; Dinghuai Zhang; Jean-Christophe Gagnon-Audet; Yoshua Bengio; Ioannis Mitliagkas; Irina Rish (NeurIPS, 2021)

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization. - Soroosh Shahtalebi; Jean-Christophe Gagnon-Audet; Touraj Laleh; Mojtaba Faramarzi; Kartik Ahuja; Irina Rish (CoRR, 2021)

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. - Germán Abrevaya; Guillaume Dumas; Aleksandr Y. Aravkin; Peng Zheng 002; Jean-Christophe Gagnon-Audet; James R. Kozloski; Pablo Polosecki; Guillaume Lajoie; David D. Cox; Silvina Ponce Dawson; Guillermo A. Cecchi; Irina Rish (Neural Comput., 2021)

A Remedy For Distributional Shifts Through Expected Domain Translation. - Jean-Christophe Gagnon-Audet; Soroosh Shahtalebi; Frank Rudzicz; Irina Rish (ICASSP, 2022)

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks. - Jean-Christophe Gagnon-Audet; Kartik Ahuja; Mohammad-Javad Darvishi Bayazi; Guillaume Dumas; Irina Rish (CoRR, 2022)

Generative Models of Brain Dynamics. - Mahta Ramezanian-Panahi; Germán Abrevaya; Jean-Christophe Gagnon-Audet; Vikram Voleti; Irina Rish; Guillaume Dumas (Frontiers Artif. Intell., 2022)

GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models. - Germán Abrevaya; Mahta Ramezanian-Panahi; Jean-Christophe Gagnon-Audet; Irina Rish; Pablo Polosecki; Silvina Ponce Dawson; Guillermo A. Cecchi; Guillaume Dumas (CoRR, 2023)

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series. - Jean-Christophe Gagnon-Audet; Kartik Ahuja; Mohammad-Javad Darvishi Bayazi; Pooneh Mousavi; Guillaume Dumas; Irina Rish (Trans. Mach. Learn. Res., 2023)

Effective Latent Differential Equation Models via Attention and Multiple Shooting. - Germán Abrevaya; Mahta Ramezanian-Panahi; Jean-Christophe Gagnon-Audet; Pablo Polosecki; Irina Rish; Silvina Ponce Dawson; Guillermo A. Cecchi; Guillaume Dumas (Trans. Mach. Learn. Res., 2024)

Scaling and Distilling Transformer Models for sEMG. - Nicholas Mehlman; Jean-Christophe Gagnon-Audet; Michael Shvartsman; Kelvin Niu; Alexander H. Miller; Shagun Sodhani (Trans. Mach. Learn. Res., 2025)

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements. - Bingchen Zhao; Despoina Magka; Minqi Jiang; Xian Li; Roberta Raileanu; Tatiana Shavrina; Jean-Christophe Gagnon-Audet; Kelvin Niu; Shagun Sodhani; Michael Shvartsman; Andrei Lupu; Alisia Maria Lupidi; et al. (CoRR, 2025)

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench. - Edan Toledo; Karen Hambardzumyan; Martin Josifoski; Rishi Hazra; Nicolas Mario Baldwin; Alexis Audran-Reiss; Michael Kuchnik; Despoina Magka; Minqi Jiang; Alisia Maria Lupidi; Andrei Lupu; Roberta Raileanu; et al. (CoRR, 2025)