298 citations · 1.2k across the 24 of their papers we have counts for
6 papers · 1 filter
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang, Jason D. Lee, Martin J. Wainwright +1
We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are -Lipschitz continuous, we present algorithms to learn ha…
-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan
We study the improper learning of multi-layer neural networks. Suppose that the neural network to be learned has hidden layers and that the -norm of the incoming weight…
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Yuchen Zhang, Michael I. Jordan
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing…
Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators
Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
For the problem of high-dimensional sparse linear regression, it is known that an -based estimator can achieve a "fast" rate on the prediction error without any condi…
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
We study the following generalized matrix rank estimation problem: given an matrix and a constant , estimate the number of eigenvalues that are greater than…
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
Yuchen Zhang, Lin Xiao
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We…