Choosing the Sample with Lowest Loss makes SGD Robust

Choosing the Sample with Lowest Loss makes SGD Robust

10 January 2020

Sujay Sanghavi

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

11 / 11 papers shown

Title
A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices Chen Gong Rui Xing Zhenzhe Zheng Fan Wu 40 0 0 22 May 2025
Geometric Median Matching for Robust k-Subset Selection from Noisy Data Anish Acharya Sujay Sanghavi Alexandros G. Dimakis Inderjit S Dhillon AAML 176 0 0 01 Apr 2025
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution Ali Mollaahmadi Dehaghi Hossein KhademSohi Reza Razavi Steve Drew Mohammad Moshirpour FedML 107 0 0 17 Mar 2025
Geometric Median (GM) Matching for Robust Data Pruning Anish Acharya Inderjit S Dhillon Sujay Sanghavi AAML 139 0 0 20 Jan 2025
Communication-Efficient Local SGD with Age-Based Worker Selection Feng Zhu Jingjing Zhang Xin Eric Wang 88 1 0 31 Oct 2022
Rank-based Decomposable Losses in Machine Learning: A Survey Shu Hu Xin Wang Siwei Lyu 79 32 0 18 Jul 2022
A Robust Optimization Method for Label Noisy Datasets Based on Adaptive Threshold: Adaptive-k Enes Dedeoglu Himmet Toprak Kesgin M. Amasyalı NoLa 42 2 0 26 Mar 2022
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods Amirkeivan Mohtashami Sebastian U. Stich Martin Jaggi 64 13 0 03 Feb 2022
Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture Zihao Deng Zee Fryer Brendan Juba Rina Panigrahy Xin Wang 55 2 0 21 Dec 2021
When Do Curricula Work? Xiaoxia Wu Ethan Dyer Behnam Neyshabur 92 118 0 05 Dec 2020
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies Yae Jee Cho Jianyu Wang Gauri Joshi FedML 160 408 0 03 Oct 2020