129 citations · 308 across the 6 of their papers we have counts for
6 papers
Accelerated Methods for Non-Convex Optimization
Yair Carmon, John C. Duchi, Oliver Hinder +1
We present an accelerated gradient method for non-convex optimization problems with Lipschitz continuous first and second derivatives. The method requires time $O(ε^{-7/4} \log(1/…
Estimation from Indirect Supervision with Linear Moments
Aditi Raghunathan, Roy Frostig, John Duchi +1
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective an…
Privacy and Statistical Risk: Formalisms and Minimax Bounds
Rina Foygel Barber, John C. Duchi
We explore and compare a variety of definitions for privacy and disclosure limitation in statistical estimation and data analysis, including (approximate) differential privacy, tes…
Optimality guarantees for distributed statistical estimation
John C. Duchi, Michael I. Jordan, Martin J. Wainwright +1
Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines. To stud…
Constrained Approximate Maximum Entropy Learning of Markov Random Fields
Varun Ganapathi, David Vickrey, John Duchi +1
Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exa…
Projected Subgradient Methods for Learning Sparse Gaussians
John Duchi, Stephen Gould, Daphne Koller
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our ap…