7 citations · 23 across the 11 of their papers we have counts for
16 papers
Learning (Very) Simple Generative Models Is Hard
Sitan Chen, Jerry Li, Yuanzhi Li
Motivated by the recent empirical successes of deep generative models, we study the computational complexity of the following unsupervised learning problem. For an unknown neural n…
Learning Polynomial Transformations
Sitan Chen, Jerry Li, Yuanzhi Li +1
We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form , where is hidden and $p:…
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
Sitan Chen, Jerry Li, Yuanzhi Li +1
Arguably the most fundamental question in the theory of generative adversarial networks (GANs) is to understand to what extent GANs can actually learn the underlying distribution.…
Efficiently Learning Any One Hidden Layer ReLU Network From Queries
Sitan Chen, Adam R Klivans, Raghu Meka
Model extraction attacks have renewed interest in the classic problem of learning neural networks from queries. In this work we give the first polynomial-time algorithm for learnin…
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization
Sitan Chen, Xiaoxiao Li, Zhao Song +1
In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the contex…
Learning Deep ReLU Networks Is Fixed-Parameter Tractable
Sitan Chen, Adam R. Klivans, Raghu Meka
We consider the problem of learning an unknown ReLU network with respect to Gaussian inputs and obtain the first nontrivial results for networks of depth more than two. We give an…