332 citations · 962 across the 43 of their papers we have counts for
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How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View
Tengyuan Liang
We study in this paper the rate of convergence for learning densities under the Generative Adversarial Networks (GAN) framework, borrowing insights from nonparametric statistics. W…
Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
Tengyuan Liang, Weijie Su
Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint only informs us how wel…
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin +1
We study the relationship between geometry and capacity measures for deep neural networks from an invariance viewpoint. We introduce a new notion of capacity --- the Fisher-Rao nor…
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connecti…
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
Satyen Kale, Zohar Karnin, Tengyuan Liang +1
Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a rea…