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Jean-Christophe Gagnon-Audet

4 accepted papers

2025

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

NeurIPS 2025spotlight

AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus on methods for improving agents' performance on MLE-bench, a challenging benchmark where agents compete in Kaggle competi…

Cited by 0SourcecodeScholar
2025

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements

NeurIPS 2025poster

Rapidly improving large language models (LLMs) have the potential to assist in scientific progress. One critical skill in this endeavor is the ability to faithfully reproduce existing work. To evaluate the capability of AI agents to reproduce complex code in an active research area, we introduce the…

Cited by 0SourcecodeScholar
2022

A Remedy For Distributional Shifts Through Expected Domain Translation

ICASSP 2022accepted

Machine learning models often fail to generalize to unseen domains due to the distributional shifts. A family of such shifts, “correlation shifts,” is caused by spurious correlations in the data. It is studied under the overarching topic of “domain generalization.” In thi…

Cited by 0SourceScholar
2021

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

NeurIPS 2021spotlight

The invariance principle from causality is at the heart of notable approaches such as invariant risk minimization (IRM) that seek to address out-of-distribution (OOD) generalization failures. Despite the promising theory, invariance principle-based approaches fail in common classification tasks, whe…

Cited by 313SourcePDFScholar