204 citations · 784 across the 42 of their papers we have counts for
5 papers · 1 filter
Stochastic Nonconvex Optimization with Large Minibatches
Weiran Wang, Nathan Srebro
We study stochastic optimization of nonconvex loss functions, which are typical objectives for training neural networks. We propose stochastic approximation algorithms which optimi…
Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis
Qingming Tang, Weiran Wang, Karen Livescu
We study the problem of acoustic feature learning in the setting where we have access to another (non-acoustic) modality for feature learning but not at test time. We use deep vari…
Efficient coordinate-wise leading eigenvector computation
Jialei Wang, Weiran Wang, Dan Garber +1
We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product. We establish global convergen…
Stochastic Canonical Correlation Analysis
Chao Gao, Dan Garber, Nathan Srebro +2
We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbi…
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
Jialei Wang, Weiran Wang, Nathan Srebro
We present and analyze an approach for distributed stochastic optimization which is statistically optimal and achieves near-linear speedups (up to logarithmic factors). Our approac…