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Leo Gagnon

2 accepted papers

2025

Does learning the right latent variables necessarily improve in-context learning?

ICML 2025poster

Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting avenues for efficiently solving new tasks. For many tasks, e.g., linear regression, the data factorizes: examples are independent given a task latent that generate…

2025

In-Context Learning and Occam's Razor

ICML 2025poster

A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best—a principle called Occam…