357 citations · 2.8k across the 94 of their papers we have counts for
8 papers · 1 filter
Non-convex Optimization for Machine Learning
Prateek Jain, Purushottam Kar
A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to capture the learning and prediction problems a…
Differentially Private Matrix Completion Revisited
Prateek Jain, Om Thakkar, Abhradeep Thakurta
We provide the first provably joint differentially private algorithm with formal utility guarantees for the problem of user-level privacy-preserving collaborative filtering. Our al…
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
Prateek Jain, Sham M. Kakade, Rahul Kidambi +3
This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares. This res…
FlashProfile: A Framework for Synthesizing Data Profiles
Saswat Padhi, Prateek Jain, Daniel Perelman +3
We address the problem of learning a syntactic profile for a collection of strings, i.e. a set of regex-like patterns that succinctly describe the syntactic variations in the strin…
Learning Mixture of Gaussians with Streaming Data
Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain
In this paper, we study the problem of learning a mixture of Gaussians with streaming data: given a stream of points in dimensions generated by an unknown mixture of sp…
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain +2
In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to $\mathit{local~strong~c…