1.6k citations · 4.2k across the 71 of their papers we have counts for
12 papers · 1 filter
Learning Sparse Low-Threshold Linear Classifiers
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro +2
We consider the problem of learning a non-negative linear classifier with a -norm of at most , and a fixed threshold, under the hinge-loss. This problem generalizes the probl…
Analysis of a randomized approximation scheme for matrix multiplication
Daniel Hsu, Sham M. Kakade, Tong Zhang
This note gives a simple analysis of a randomized approximation scheme for matrix multiplication proposed by Sarlos (2006) based on a random rotation followed by uniform column sam…
Tensor decompositions for learning latent variable models
Anima Anandkumar, Rong Ge, Daniel Hsu +2
This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden…
Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Animashree Anandkumar, Daniel Hsu, Adel Javanmard +1
Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled appr…
A concentration theorem for projections
Sanjoy Dasgupta, Daniel Hsu, Nakul Verma
X in R^D has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into R^d (for d < D) look like a scale-mixture o…
Convergence Rates for Differentially Private Statistical Estimation
Kamalika Chaudhuri, Daniel Hsu
Differential privacy is a cryptographically-motivated definition of privacy which has gained significant attention over the past few years. Differentially private solutions enforce…