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cs.LG2025
Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective
Yuling Jiao, Yanming Lai, Yang Wang +1
The Transformer model is widely used in various application areas of machine learning, such as natural language processing. This paper investigates the approximation of the Hölder…
stat.ML2025
Approximation Bounds for Transformer Networks with Application to Regression
Yuling Jiao, Yanming Lai, Defeng Sun +2
We explore the approximation capabilities of Transformer networks for Hölder and Sobolev functions, and apply these results to address nonparametric regression estimation with depe…
stat.ML2025
Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
Yuling Jiao, Wensen Ma, Defeng Sun +2
Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified. We formulate representation learning as Distribution Matching…