240 citations · 899 across the 24 of their papers we have counts for
10 papers · 1 filter
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
Jialei Wang, Jason D. Lee, Mehrdad Mahdavi +2
Sketching techniques have become popular for scaling up machine learning algorithms by reducing the sample size or dimensionality of massive data sets, while still maintaining the…
Warm Starting Bayesian Optimization
Matthias Poloczek, Jialei Wang, Peter I. Frazier
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related proble…
Efficient Distributed Learning with Sparsity
Jialei Wang, Mladen Kolar, Nathan Srebro +1
We propose a novel, efficient approach for distributed sparse learning in high-dimensions, where observations are randomly partitioned across machines. Computationally, at each rou…
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Jialei Wang, Peder A. Olsen, Andrew R. Conn +1
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and remo…
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
Shun Zheng, Jialei Wang, Fen Xia +2
In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines. It is customary to employ the "data parallelism" appro…
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Weiran Wang, Jialei Wang, Dan Garber +1
We study the stochastic optimization of canonical correlation analysis (CCA), whose objective is nonconvex and does not decouple over training samples. Although several stochastic…