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Safe Adaptive Importance Sampling

Safe Adaptive Importance Sampling

7 November 2017
Sebastian U. Stich
Anant Raj
Martin Jaggi
ArXivPDFHTML

Papers citing "Safe Adaptive Importance Sampling"

6 / 6 papers shown
Title
Information FOMO: The unhealthy fear of missing out on information. A
  method for removing misleading data for healthier models
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models
Ethan Pickering
T. Sapsis
16
6
0
27 Aug 2022
DELTA: Diverse Client Sampling for Fasting Federated Learning
DELTA: Diverse Client Sampling for Fasting Federated Learning
Lung-Chuang Wang
Yongxin Guo
Tao R. Lin
Xiaoying Tang
FedML
21
20
0
27 May 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao
Lingxiao Wang
Mladen Kolar
Ziqi Liu
Zhiqiang Zhang
Jun Zhou
Chaochao Chen
FedML
26
9
0
28 Dec 2021
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance
  tradeoff
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff
Anna Korba
Franccois Portier
14
12
0
29 Oct 2021
Optimal Importance Sampling for Federated Learning
Optimal Importance Sampling for Federated Learning
Elsa Rizk
Stefan Vlaski
A. H. Sayed
FedML
32
45
0
26 Oct 2020
A simpler approach to obtaining an O(1/t) convergence rate for the
  projected stochastic subgradient method
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
Simon Lacoste-Julien
Mark W. Schmidt
Francis R. Bach
113
259
0
10 Dec 2012
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