# ES-MAML: Simple Hessian-Free Meta Learning

arXiv: https://arxiv.org/abs/1910.01215 · HTML: https://mlsanity.com/paper/1910.01215/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 30 of 57)

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- [On the Convergence Theory for Hessian-Free Bilevel Algorithms](https://mlsanity.com/paper/2110.07004/) (2021)
- [On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2102.10454/) (2021)
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- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
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- [TorchOpt: An Efficient Library for Differentiable Optimization](https://mlsanity.com/paper/2211.06934/) (2022)
- [Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles](https://mlsanity.com/paper/2306.14853/) (2023)
- [Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD](https://mlsanity.com/paper/2109.07497/) (2021)
- [Compositional federated learning: Applications in distributionally robust averaging and meta learning](https://mlsanity.com/paper/2106.11264/) (2021)
- [A CMDP-within-online framework for Meta-Safe Reinforcement Learning](https://mlsanity.com/paper/2405.16601/) (2024)
- [Large-Scale Meta-Learning with Continual Trajectory Shifting](https://mlsanity.com/paper/2102.07215/) (2021)
- [MAML is a Noisy Contrastive Learner in Classification](https://mlsanity.com/paper/2106.15367/) (2021)
- [Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks](https://mlsanity.com/paper/2107.02378/) (2021)
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- [Few-shot Quality-Diversity Optimization](https://mlsanity.com/paper/2109.06826/) (2021)
- [A First Order Meta Stackelberg Method for Robust Federated Learning](https://mlsanity.com/paper/2306.13800/) (2023)
- [Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning](https://mlsanity.com/paper/2106.04911/) (2021)
- [Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks](https://mlsanity.com/paper/2210.12669/) (2022)
- [Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing](https://mlsanity.com/paper/2404.00158/) (2024)
- [Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning](https://mlsanity.com/paper/2502.02332/) (2025)
- [Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning](https://mlsanity.com/paper/2109.03554/) (2021)
- [Meta-Learning with Adjoint Methods](https://mlsanity.com/paper/2110.08432/) (2021)
- [Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning](https://mlsanity.com/paper/2112.08588/) (2021)
- [Discovering Adaptable Symbolic Algorithms from Scratch](https://mlsanity.com/paper/2307.16890/) (2023)
- [A New First-Order Meta-Learning Algorithm with Convergence Guarantees](https://mlsanity.com/paper/2409.03682/) (2024)

## Abstract (arXiv)

We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement. Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable. We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.