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A Communication Efficient Collaborative Learning Framework for Distributed Features
Yang Liu, Yan Kang, Xinwei Zhang +5
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw…
On Lower Iteration Complexity Bounds for the Saddle Point Problems
Junyu Zhang, Mingyi Hong, Shuzhong Zhang
In this paper, we study the lower iteration complexity bounds for finding the saddle point of a strongly convex and strongly concave saddle point problem: . We…
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller +2
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework. Recently physics…
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Guoyong Zhang, Xiao Fu, Jun Wang +2
Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)---from limited samples taken spars…
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Xiangyi Chen, Sijia Liu, Kaidi Xu +4
The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optim…
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach
Haoran Sun, Songtao Lu, Mingyi Hong
Many modern large-scale machine learning problems benefit from decentralized and stochastic optimization. Recent works have shown that utilizing both decentralized computing and lo…