27 citations · 33 across the 4 of their papers we have counts for
11 papers
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Xiao Wang, Qi Lei, Ioannis Panageas
Sampling is a fundamental and arguably very important task with numerous applications in Machine Learning. One approach to sample from a high dimensional distribution for…
CAT: Customized Adversarial Training for Improved Robustness
Minhao Cheng, Qi Lei, Pin-Yu Chen +2
Adversarial training has become one of the most effective methods for improving robustness of neural networks. However, it often suffers from poor generalization on both clean and…
Few-Shot Learning via Learning the Representation, Provably
Simon S. Du, Wei Hu, Sham M. Kakade +2
This paper studies few-shot learning via representation learning, where one uses source tasks with data per task to learn a representation in order to reduce the sample c…
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Jiacheng Zhuo, Qi Lei, Alexandros G. Dimakis +1
Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown d…
SGD Learns One-Layer Networks in WGANs
Qi Lei, Jason D. Lee, Alexandros G. Dimakis +1
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require so…
Inverting Deep Generative models, One layer at a time
Qi Lei, Ajil Jalal, Inderjit S. Dhillon +1
We study the problem of inverting a deep generative model with ReLU activations. Inversion corresponds to finding a latent code vector that explains observed measurements as much a…