First-Order Adaptive Sample Size Methods to Reduce Complexity of
Empirical Risk Minimization

First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization

2 September 2017

Alejandro Ribeiro

Papers citing "First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization"

5 / 5 papers shown

Title
Sampling Through the Lens of Sequential Decision Making J. Dou Alvin Pan Runxue Bao Haiyi Mao Lei Luo Zhi-Hong Mao 26 19 0 17 Aug 2022
Constrained and Composite Optimization via Adaptive Sampling Methods Yuchen Xie Raghu Bollapragada R. Byrd J. Nocedal 27 14 0 31 Dec 2020
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature Franz J. Király Bilal A. Mateen R. Sonabend 23 10 0 18 Dec 2018
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy Majid Jahani Xi He Chenxin Ma Aryan Mokhtari Dheevatsa Mudigere Alejandro Ribeiro Martin Takáč 30 18 0 26 Oct 2018
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen Aryan Mokhtari Alejandro Ribeiro 38 16 0 22 May 2017