167 citations · 178 across the 2 of their papers we have counts for
5 papers
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Learning Distributions Generated by One-Layer ReLU Networks
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
We consider the problem of estimating the parameters of a -dimensional rectified Gaussian distribution from i.i.d. samples. A rectified Gaussian distribution is defined by passi…
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
We characterize the effectiveness of a classical algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples. The algorithm is (app…
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi +5
Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper…
Leveraging Sparsity for Efficient Submodular Data Summarization
Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis
The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering. One difficulty of…