Safe Adaptive Importance Sampling

7 November 2017

Papers citing "Safe Adaptive Importance Sampling"

15 / 15 papers shown

Title
Importance Sampling for Nonlinear Models Prakash Palanivelu Rajmohan Fred Roosta 7 0 0 18 May 2025
Recent Advances in Path Integral Control for Trajectory Optimization: An Overview in Theoretical and Algorithmic Perspectives Muhammad Kazim JunGee Hong Min-Gyeom Kim Kwang-Ki K. Kim 39 16 0 22 Sep 2023
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models Ethan Pickering T. Sapsis 24 6 0 27 Aug 2022
DELTA: Diverse Client Sampling for Fasting Federated Learning Lung-Chuang Wang Yongxin Guo Tao R. Lin Xiaoying Tang FedML 23 22 0 27 May 2022
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods Amirkeivan Mohtashami Sebastian U. Stich Martin Jaggi 26 13 0 03 Feb 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 34 10 0 28 Dec 2021
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff Anna Korba Franccois Portier 28 12 0 29 Oct 2021
Federated Learning under Importance Sampling Elsa Rizk Stefan Vlaski Ali H. Sayed FedML 18 52 0 14 Dec 2020
FairBatch: Batch Selection for Model Fairness Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh VLM 14 128 0 03 Dec 2020
Optimal Client Sampling for Federated Learning Wenlin Chen Samuel Horváth Peter Richtárik FedML 42 191 0 26 Oct 2020
Optimal Importance Sampling for Federated Learning Elsa Rizk Stefan Vlaski Ali H. Sayed FedML 40 46 0 26 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 34 0 0 26 Aug 2020
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets Ilqar Ramazanli Han Nguyen Hai Pham Sashank J. Reddi Barnabás Póczós 23 11 0 20 Feb 2020
Accelerating Stochastic Gradient Descent Using Antithetic Sampling Jingchang Liu Linli Xu 19 2 0 07 Oct 2018
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 128 259 0 10 Dec 2012