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Tengyuan Liang
Bio
Research
Teaching
Talks
Vitae
Machine Learning (stat.ML)
Online Learning to Transport via the Minimal Selection Principle
Universal Prediction Band via Semi-Definite Programming
A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers
Reversible Gromov-Monge Sampler for Simulation-Based Inference
How Well Generative Adversarial Networks Learn Distributions
Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
Interpolating Classifiers Make Few Mistakes
Deep Neural Networks for Estimation and Inference
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
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