3 papers
cs.LG2026
Learning is Forgetting: LLM Training As Lossy Compression
Henry C. Conklin, Tom Hosking, Tan Yi-Chern +5
Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are structured. This limits our abilit…
cs.LG2025
Hierarchical Refinement: Optimal Transport to Infinity and Beyond
Peter Halmos, Julian Gold, Xinhao Liu +1
Optimal transport (OT) has enjoyed great success in machine learning as a principled way to align datasets via a least-cost correspondence, driven in large part by the runtime effi…
cs.LG2024
Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling
Peter Halmos, Xinhao Liu, Julian Gold +1
Optimal transport (OT) is a general framework for finding a minimum-cost transport plan, or coupling, between probability distributions, and has many applications in machine learni…