# Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems

arXiv: https://arxiv.org/abs/2210.02515 · HTML: https://mlsanity.com/paper/2210.02515/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2022.

## Built on by (most cited first, 13 of 13)

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- [A Generalized Alternating Method for Bilevel Learning under the Polyak-Lojasiewicz Condition](https://mlsanity.com/paper/2306.02422/) (2023)
- [Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization](https://mlsanity.com/paper/2401.06980/) (2024)
- [Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs](https://mlsanity.com/paper/2406.11569/) (2024)
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- [Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations](https://mlsanity.com/paper/2607.26481/) (2026)
- [Decision Feedback In-Context Learning for Wireless Symbol Detection](https://mlsanity.com/paper/2503.16594/) (2025)
- [Meta-Learning-Based Handover Management in NextG O-RAN](https://mlsanity.com/paper/2512.22022/) (2025)
- [Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control](https://mlsanity.com/paper/2607.15412/) (2026)

## Abstract (arXiv)

Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are often difficult to translate into real-world engineering systems because deep learning models require a massive number of training samples, which are costly to obtain in practice. To address labeled data scarcity, few-shot meta-learning optimizes learning algorithms that can efficiently adapt to new tasks quickly. While meta-learning is gaining significant interest in the machine learning literature, its working principles and theoretic fundamentals are not as well understood in the engineering community. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications. After introducing meta-learning in comparison with conventional and joint learning, we describe the main meta-learning algorithms, as well as a general bilevel optimization framework for the definition of meta-learning techniques. Then, we summarize known results on the generalization capabilities of meta-learning from a statistical learning viewpoint. Applications to communication systems, including decoding and power allocation, are discussed next, followed by an introduction to aspects related to the integration of meta-learning with emerging computing technologies, namely neuromorphic and quantum computing. The monograph is concluded with an overview of open research challenges.