258 citations · 935 across the 92 of their papers we have counts for
8 papers · 1 filter
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge
Benjamin Coleman, Gaurav Gupta, John Chen +1
Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression…
A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models
Ryan Spring, Anshumali Shrivastava
Log-linear models are arguably the most successful class of graphical models for large-scale applications because of their simplicity and tractability. Learning and inference with…
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
Ping Li, Michael Mitzenmacher, Anshumali Shrivastava
The method of random projections has become a standard tool for machine learning, data mining, and search with massive data at Web scale. The effective use of random projections re…
Scalable and Sustainable Deep Learning via Randomized Hashing
Ryan Spring, Anshumali Shrivastava
Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions…
Asymmetric Minwise Hashing
Anshumali Shrivastava, Ping Li
Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications whe…
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
Anshumali Shrivastava, Ping Li
Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before h…