xiaofangxd / natural-evolution-strategy-and-learning-to-optimize

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

• Natural Evolution Strategy
• Learn to Optimize

Natural-Evolution-Strategy

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	arxiv	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	arxiv	2022	Hu et al.	[paper] [code]
Mirror Natural Evolution Strategies	arxiv	2023	Ye et al.	[paper]
Decentralized projected Riemannian gradient method for smooth optimization on compact submanifolds	arxiv	2023	Deng et al.	[paper]
Decentralized Riemannian natural gradient methods with Kronecker-product approximations	arxiv	2023	Hu et al.	[paper]

Learn-to-Optimize

Paper Title	Venue	Year	Authors	Materials	Comment
B2Opt: Learning to Optimize Black-box Optimization with Little Budget	arxiv	2023	Li et al.	[paper]
Transformer-Based Learned Optimization	CVPR	2023	Gärtner et al.	[paper]
Discovering Evolution Strategies via Meta-Black-Box Optimization	ICLR	2023	Lange et al.	[paper]
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization	GECCO	2023	Lange et al.	[paper]
Explainable AI via Learning to Optimize	Scientific Reports	2023	Heaton et al.	[paper] [code]
Learning to Optimize: A Primer and A Benchmark	JMLR	2022	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	arxiv	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	arxiv	2016	Li et al.	[paper]

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