Stochastic Bias-Reduced Gradient Methods

17 June 2021

Papers citing "Stochastic Bias-Reduced Gradient Methods"

20 / 20 papers shown

Title
Unbiased least squares regression via averaged stochastic gradient descent Nabil Kahalé 18 0 0 26 Jun 2024
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization A. Jambulapati Aaron Sidford Kevin Tian 50 1 0 11 Jun 2024
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization Yexin Zhang Chenyi Zhang Cong Fang Liwei Wang Tongyang Li 37 2 0 05 Jun 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions Hilal Asi Daogao Liu Kevin Tian 40 3 0 04 Jun 2024
When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones? Guanyang Wang Jose Blanchet Peter Glynn 20 1 0 01 Apr 2024
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems Tomás González Cristóbal Guzmán Courtney Paquette 34 3 0 05 Mar 2024
Extending the Reach of First-Order Algorithms for Nonconvex Min-Max Problems with Cohypomonotonicity Ahmet Alacaoglu Donghwan Kim Stephen J. Wright 27 3 0 07 Feb 2024
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers Ron Dorfman Naseem Yehya Kfir Y. Levy 22 2 0 05 Feb 2024
Optimal Multi-Distribution Learning Zihan Zhang Wenhao Zhan Yuxin Chen Simon S. Du Jason D. Lee 31 12 0 08 Dec 2023
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 21 4 0 17 Nov 2023
Contextual Stochastic Bilevel Optimization Yifan Hu Jie Wang Yao Xie Andreas Krause Daniel Kuhn 34 11 0 27 Oct 2023
ReSQueing Parallel and Private Stochastic Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Y. Lee Daogao Liu Aaron Sidford Kevin Tian FedML 22 12 0 01 Jan 2023
Interpreting Bias in the Neural Networks: A Peek Into Representational Similarity Gnyanesh Bangaru Lalith Bharadwaj Baru Kiran Chakravarthula 17 0 0 14 Nov 2022
Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization Le‐Yu Chen Luo Luo 34 7 0 11 Aug 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 50 11 0 17 Jun 2022
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization Liang Zhang K. K. Thekumparampil Sewoong Oh Niao He 36 17 0 01 Jun 2022
Distributionally Robust Optimization via Ball Oracle Acceleration Y. Carmon Danielle Hausler 18 11 0 24 Mar 2022
Adapting to Mixing Time in Stochastic Optimization with Markovian Data Ron Dorfman Kfir Y. Levy 37 28 0 09 Feb 2022
Unbiased Optimal Stopping via the MUSE Zhengqing Zhou Guanyang Wang Jose H. Blanchet Peter Glynn 19 6 0 04 Jun 2021
Global Riemannian Acceleration in Hyperbolic and Spherical Spaces David Martínez-Rubio 32 19 0 07 Dec 2020