167 citations · 293 across the 8 of their papers we have counts for
8 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…
Federated Reconstruction: Partially Local Federated Learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett +3
Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client hetero…
Implicit Regularization and Convergence for Weight Normalization
Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren +5
Normalization methods such as batch [Ioffe and Szegedy, 2015], weight [Salimansand Kingma, 2016], instance [Ulyanov et al., 2016], and layer normalization [Baet al., 2016] have bee…
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…
Single Pass PCA of Matrix Products
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi +1
In this paper we present a new algorithm for computing a low rank approximation of the product by taking only a single pass of the two matrices and . The straightforw…