activity
20172020
most citedCoding Method for Parallel Iterative Linear Solver

9 citations · 15 across the 2 of their papers we have counts for

collaborators

9 papers

cs.LG2020

Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models

Zhengming Zhang, Yaoqing Yang, Zhewei Yao +3

Federated learning (FL) is a promising way to use the computing power of mobile devices while maintaining the privacy of users. Current work in FL, however, makes the unrealistic a…

cs.LG2020

Boundary thickness and robustness in learning models

Yaoqing Yang, Rajiv Khanna, Yaodong Yu +5

Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thi…

cs.DC20206 cited

Serverless Straggler Mitigation using Local Error-Correcting Codes

Vipul Gupta, Dominic Carrano, Yaoqing Yang +3

Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation. We propose and impl…

cs.CV2019

Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

Siheng Chen, Chaojing Duan, Yaoqing Yang +3

We propose a deep autoencoder with graph topology inference and filtering to achieve compact representations of unorganized 3D point clouds in an unsupervised manner. Many previous…

cs.IT2018

Coded Elastic Computing

Yaoqing Yang, Matteo Interlandi, Pulkit Grover +3

Cloud providers have recently introduced new offerings whereby spare computing resources are accessible at discounts compared to on-demand computing. Exploiting such opportunity is…

cs.IT2018

An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation

Utsav Sheth, Sanghamitra Dutta, Malhar Chaudhari +5

We propose a novel application of coded computing to the problem of the nearest neighbor estimation using MatDot Codes [Fahim. et.al. 2017], that are known to be optimal for matrix…