Publications (28)
Non-convex Robust PCA
Praneeth Netrapalli, U N Niranjan, Sujay Sanghavi +2
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain +1
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Information-theoretic thresholds for community detection in sparse networks
Jess Banks, Cristopher Moore, Joe Neeman +1
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
Finding the Graph of Epidemic Cascades
Praneeth Netrapalli, Sujay Sanghavi
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
Chi Jin, Praneeth Netrapalli, Rong Ge +2
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Alekh Agarwal, Animashree Anandkumar, Prateek Jain +1
Phase Retrieval using Alternating Minimization
Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
Accelerating Stochastic Gradient Descent For Least Squares Regression
Prateek Jain, Sham M. Kakade, Rahul Kidambi +2
Greedy Learning of Markov Network Structure
Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi +1
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Chi Jin, Sham M. Kakade, Praneeth Netrapalli
Learning Planar Ising Models
Jason K. Johnson, Praneeth Netrapalli, Michael Chertkov
Efficient Algorithms for Smooth Minimax Optimization
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli +1
Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification
Prateek Jain, Sham M. Kakade, Rahul Kidambi +2
Leverage Score Sampling for Faster Accelerated Regression and ERM
Naman Agarwal, Sham Kakade, Rahul Kidambi +3
Learning Planar Ising Models
Jason K. Johnson, Diane Oyen, Michael Chertkov +1
Non-Reconstructability in the Stochastic Block Model
Joe Neeman, Praneeth Netrapalli
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
Prateek Jain, Chi Jin, Sham M. Kakade +2
Convergence Rates of Active Learning for Maximum Likelihood Estimation
Kamalika Chaudhuri, Sham Kakade, Praneeth Netrapalli +1
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Rong Ge, Chi Jin, Sham M. Kakade +2
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
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot
Prateek Jain, Chi Jin, Sham M. Kakade +1
Making the Last Iterate of SGD Information Theoretically Optimal
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Thresholding based Efficient Outlier Robust PCA
Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
How to Escape Saddle Points Efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli +2
On the insufficiency of existing momentum schemes for Stochastic Optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain +1
Low-rank Matrix Completion using Alternating Minimization
Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi