32 citations · 140 across the 16 of their papers we have counts for
7 papers · 2 filters
ASVRG: Accelerated Proximal SVRG
Fanhua Shang, Licheng Jiao, Kaiwen Zhou +3
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick. Unlike most…
Norm-Range Partition: A Universal Catalyst for LSH based Maximum Inner Product Search (MIPS)
Xiao Yan, Xinyan Dai, Jie Liu +2
Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including -ALSH, Sign-ALSH and Simple-LSH have been proposed. In this pape…
Norm-Ranging LSH for Maximum Inner Product Search
Xiao Yan, Jinfeng Li, Xinyan Dai +2
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee. We found that the perform…
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
Fanhua Shang, James Cheng, Yuanyuan Liu +2
The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-level vision have proven effective priors for many applications such as background m…
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Kaiwen Zhou, Fanhua Shang, James Cheng
Recent years have witnessed exciting progress in the study of stochastic variance reduced gradient methods (e.g., SVRG, SAGA), their accelerated variants (e.g, Katyusha) and their…
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
Fanhua Shang, Yuanyuan Liu, James Cheng
The Schatten quasi-norm was introduced to bridge the gap between the trace norm and rank function. However, existing algorithms are too slow or even impractical for large-scale pro…