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stat.ML2026
Optimal Transport for Machine Learners
Gabriel Peyré, Gabriel Peyré
Modern machine learning repeatedly manipulates probability measures: empirical datasets, generated samples, latent distributions, class-conditional laws, particle systems, weights…
stat.ML2025
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…