# Gradient-EM Bayesian Meta-learning

arXiv: https://arxiv.org/abs/2006.11764 · HTML: https://mlsanity.com/paper/2006.11764/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms](https://mlsanity.com/paper/2103.03697/) (2021)
- [Transfering Hierarchical Structure with Dual Meta Imitation Learning](https://mlsanity.com/paper/2201.11981/) (2022)
- [Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2208.01573/) (2022)
- [Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review](https://mlsanity.com/paper/2111.12146/) (2021)
- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)

## Abstract (arXiv)

Bayesian meta-learning enables robust and fast adaptation to new tasks with uncertainty assessment. The key idea behind Bayesian meta-learning is empirical Bayes inference of hierarchical model. In this work, we extend this framework to include a variety of existing methods, before proposing our variant based on gradient-EM algorithm. Our method improves computational efficiency by avoiding back-propagation computation in the meta-update step, which is exhausting for deep neural networks. Furthermore, it provides flexibility to the inner-update optimization procedure by decoupling it from meta-update. Experiments on sinusoidal regression, few-shot image classification, and policy-based reinforcement learning show that our method not only achieves better accuracy with less computation cost, but is also more robust to uncertainty.