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Dong Yin
Observed name:
Yin, Dong
Institution:
Apple (United States)
Observed institutions:
Google (United States),
University of Alberta,
Princeton University,
Google DeepMind (United Kingdom),
Apple (United States),
University of California, Berkeley,
Tsinghua University
show all 7
ORCID:
0000-0002-2358-0816
Observed ORCID:
0000-0002-2358-0816
OpenAlex ID:
A5001139821
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
2018
·
Yin, Dong
, Chen, Yudong
, et al.
·
arXiv (Cornell University)
·
1,199
PDF
An Efficient Framework for Clustered Federated Learning
2022
·
Avishek Ghosh
, Jichan Chung
, et al.
·
IEEE Transactions on Information Theory
·
962
PDF
A Fourier Perspective on Model Robustness in Computer Vision
2019
·
Yin, Dong
, Lopes, Raphael Gontijo
, et al.
·
arXiv (Cornell University)
·
355
PDF
Robust Federated Learning in a Heterogeneous Environment
2019
·
Ghosh, Avishek
, Hong, Justin
, et al.
·
arXiv (Cornell University)
·
260
PDF
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
2019
·
Lopes, Raphael Gontijo
, Yin, Dong
, et al.
·
arXiv (Cornell University)
·
222
PDF
Rademacher Complexity for Adversarially Robust Generalization
2018
·
Yin, Dong
, Ramchandran, Kannan
, et al.
·
arXiv (Cornell University)
·
156
PDF
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning
2017
·
Yin, Dong
, Pananjady, Ashwin
, et al.
·
arXiv (Cornell University)
·
109
PDF
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
2018
·
Yin, Dong
, Chen, Yudong
, et al.
·
arXiv (Cornell University)
·
71
PDF
PhaseCode: Fast and Efficient Compressive Phase Retrieval Based on Sparse-Graph Codes
2014
·
Ramtin Pedarsani
, Dong Yin
, et al.
·
IEEE Transactions on Information Theory
·
54
PDF
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint
2020
·
Yin, Dong
, Farajtabar, Mehrdad
, et al.
·
arXiv (Cornell University)
·
51
PDF
The Effectiveness of Memory Replay in Large Scale Continual Learning
2020
·
Balaji, Yogesh
, Farajtabar, Mehrdad
, et al.
·
arXiv (Cornell University)
·
36
PDF
Sub-Linear Time Support Recovery for Compressed Sensing Using Sparse-Graph Codes
2019
·
Xiao Li
, Dong Yin
, et al.
·
IEEE Transactions on Information Theory
·
31
PDF
An instance-dependent simulation framework for learning with label noise
2022
·
Keren Gu
, Xander Masotto
, et al.
·
Machine Learning
·
29
PDF
Stochastic Gradient and Langevin Processes
2019
·
Cheng, Xiang
, Yin, Dong
, et al.
·
arXiv (Cornell University)
·
20
PDF
Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes
2018
·
Dong Yin
, Ramtin Pedarsani
, et al.
·
IEEE Transactions on Information Theory
·
20
PDF
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
2016
·
Charles, Adam
, Yin, Dong
, et al.
·
arXiv (Cornell University)
·
17
PDF
SOLA: Continual Learning with Second-Order Loss Approximation.
2020
·
Dong Yin
, Mehrdad Farajtabar
, et al.
·
arXiv (Cornell University)
·
13
Fast and robust compressive phase retrieval with sparse-graph codes
2015
·
Dong Yin
, Kangwook Lee
, et al.
·
IEEE International Symposium on Information Theory (ISIT)
·
13
PDF
Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo
2024
·
Feng, Shengyu
, Kong, Xiang
, et al.
·
arXiv (Cornell University)
·
9
PDF
Sparse constraint affine projection algorithm with parallel implementation and application in compressive sensing
2014
·
Dong Yin
, H. C. So
, et al.
·
IEEE International Conference on Acoustics Speech and Signal Processing
·
7
Improved Regret Bound and Experience Replay in Regularized Policy Iteration
2021
·
Lazic, Nevena
, Yin, Dong
, et al.
·
arXiv (Cornell University)
·
5
PDF
Quantitative W 1 Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise.
2019
·
Xiang Cheng
, Dong Yin
, et al.
·
arXiv (Cornell University)
·
5
Compressed sensing using sparse-graph codes for the continuous-alphabet setting
2016
·
Dong Yin
, Ramtin Pedarsani
, et al.
·
Allerton Conference on Communication, Control, and Computing
·
5
Online Learning for Non-Stationary A/B Tests
2018
·
Andrés Muñoz Medina
, Sergei Vassilvitskii
, et al.
·
ACM International Conference on Information and Knowledge Management (CIKM)
·
4
PDF
Learning mixtures of sparse linear regressions using sparse graph codes
2017
·
Dong Yin
, Ramtin Pedarsani
, et al.
·
Allerton Conference on Communication, Control, and Computing
·
1
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Works count:
28
Citation count:
3,654
H-index:
16
I10-index:
18
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