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Choosing the Sample with Lowest Loss makes SGD Robust

Choosing the Sample with Lowest Loss makes SGD Robust

10 January 2020
Vatsal Shah
Xiaoxia Wu
Sujay Sanghavi
ArXivPDFHTML

Papers citing "Choosing the Sample with Lowest Loss makes SGD Robust"

12 / 12 papers shown
Title
Geometric Median Matching for Robust k-Subset Selection from Noisy Data
Geometric Median Matching for Robust k-Subset Selection from Noisy Data
Anish Acharya
Sujay Sanghavi
Alexandros G. Dimakis
Inderjit S Dhillon
AAML
62
0
0
01 Apr 2025
Geometric Median (GM) Matching for Robust Data Pruning
Geometric Median (GM) Matching for Robust Data Pruning
Anish Acharya
Inderjit S Dhillon
Sujay Sanghavi
AAML
59
0
0
20 Jan 2025
REALM: Robust Entropy Adaptive Loss Minimization for Improved
  Single-Sample Test-Time Adaptation
REALM: Robust Entropy Adaptive Loss Minimization for Improved Single-Sample Test-Time Adaptation
Skyler Seto
B. Theobald
Federico Danieli
Navdeep Jaitly
Dan Busbridge
TTA
OOD
45
6
0
07 Sep 2023
On The Impact of Machine Learning Randomness on Group Fairness
On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh
Hong Chang
Martin Strobel
Reza Shokri
FaML
33
30
0
09 Jul 2023
AdaSelection: Accelerating Deep Learning Training through Data
  Subsampling
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
Minghe Zhang
Chaosheng Dong
Jinmiao Fu
Tianchen Zhou
Jia Liang
...
Bo Liu
Michinari Momma
Bryan Wang
Yan Gao
Yi Sun
35
3
0
19 Jun 2023
Communication-Efficient Local SGD with Age-Based Worker Selection
Communication-Efficient Local SGD with Age-Based Worker Selection
Feng Zhu
Jingjing Zhang
Xin Wang
35
1
0
31 Oct 2022
Rank-based Decomposable Losses in Machine Learning: A Survey
Rank-based Decomposable Losses in Machine Learning: A Survey
Shu Hu
Xin Wang
Siwei Lyu
38
32
0
18 Jul 2022
Streaming Algorithms for High-Dimensional Robust Statistics
Streaming Algorithms for High-Dimensional Robust Statistics
Ilias Diakonikolas
D. Kane
Ankit Pensia
Thanasis Pittas
19
21
0
26 Apr 2022
Characterizing & Finding Good Data Orderings for Fast Convergence of
  Sequential Gradient Methods
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
Amirkeivan Mohtashami
Sebastian U. Stich
Martin Jaggi
23
13
0
03 Feb 2022
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong
Xiaojie Jin
Weihao Gao
Yijia Wang
Hongyi Zhang
Xiang Wu
Jianchao Yang
Xiaobing Liu
28
5
0
27 Apr 2021
Client Selection in Federated Learning: Convergence Analysis and
  Power-of-Choice Selection Strategies
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Yae Jee Cho
Jianyu Wang
Gauri Joshi
FedML
44
400
0
03 Oct 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
139
1,201
0
16 Aug 2016
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