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stat.ML2025
Optimal Transport for Machine Learners
Gabriel Peyré
Modern machine learning repeatedly manipulates probability measures: empirical datasets, generated samples, latent distributions, class-conditional laws, particle systems, weights…
stat.ML2024
Towards Understanding the Universality of Transformers for Next-Token Prediction
Michael E. Sander, Gabriel Peyré
Causal Transformers are trained to predict the next token for a given context. While it is widely accepted that self-attention is crucial for encoding the causal structure of seque…
stat.ML2024
How do Transformers perform In-Context Autoregressive Learning?
Michael E. Sander, Raja Giryes, Taiji Suzuki +2
Transformers have achieved state-of-the-art performance in language modeling tasks. However, the reasons behind their tremendous success are still unclear. In this paper, towards a…