Natural Evolution Strategy and Learning to Optimize

Posted: October 05, 2022

Status: Completed

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A Collection of Main Papers on Natural Evolution Strategy and Learning to Optimize

Paper Title	Venue	Year	Authors	Materials	Comment
Natural Evolution Strategies	JMLR	2014	Wierstra et al.	[paper] [Python code] [MATLAB code]	[blog] [Chinese blog]
The CMA Evolution Strategy: A Tutorial	arkiv	2016	Hansen et al.	[paper] [Python code] [MATLAB code]
Information-geometric optimization algorithms: A unifying picture via invariance principles	JMLR	2017	Ollivier et al.	[paper] [code]
Kernelized Wasserstein Natural Gradient	ICLR	2020	Arbel et al.	[paper] [code]
The Variational Predictive Natural Gradient	ICML	2020	Tang et al.	[paper] [code]
The Hessian Estimation Evolution Strategy & Convergence Analysis of the Hessian Estimation Evolution Strategy	PPSN & EC	2020 & 2022	Glasmachers et al.	[HEES] [Convergence Analysis] [code]
General Univariate Estimation-of-Distribution Algorithms	PPSN	2022	Doerr et al.	[paper]
Adaptive Evolution Strategies for Stochastic Zeroth-Order Optimization	IEEE TETCI	2022	He et al.	[paper] [code]
Riemannian Natural Gradient Methods	arkiv	2022	Hu et al.	[paper] [code]

Paper Title	Venue	Year	Authors	Materials
Explainable AI via Learning to Optimize	arkiv	2022	Heaton et al.	[paper] [code]
Learning to Optimize: A Primer and A Benchmark	arkiv	2021	Chen et al.	[paper] [code]
Learning A Minimax Optimizer: A Pilot Study	ICLR	2021	Shen et al.	[paper] [code]
Meta Learning Black-Box Population-Based Optimizers	arkiv	2021	SGomes et al.	[paper] [code]
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks	NIPS	2021	Yatsura et al.	[paper] [code]
Training Stronger Baselines for Learning to Optimize	NIPS	2020	Chen et al.	[paper] [code]
Meta-Learning for Black-Box Optimization	ECML	2020	TV et al.	[paper] [code]
Learning to Optimize in Swarms	NIPS	2019	Cao et al.	[paper] [code]
Learning to Optimize Combinatorial Functions	ICML	2018	Rosenfeld et al.	[paper]
Learning to Optimize	arkiv	2016	Li et al.	[paper]

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