Meta-learning, or learning to learn, is a rapidly evolving area in machine learning that aims to enhance the adaptability and efficiency of learning algorithms. Inspired by the human ability to learn new concepts from limited examples and quickly adapt to unforeseen situations, meta-learning leverages prior experience to prepare models for fast adaptation to new tasks. Unlike traditional machine learning systems, where models are trained for specific tasks, meta-learning frameworks enable models to acquire generalized knowledge during training and efficiently learn new tasks during inference. This ability to generalize from past experiences to new tasks makes meta-learning a key focus in advancing artificial intelligence, offering the potential to create more flexible and efficient AI systems capable of performing well with minimal data.

In this thesis, we begin by formally defining the meta-learning framework, establishing clear terminology, and synthesizing existing work in a comprehensive survey paper. Building on this foundation, we demonstrate how meta-learning can be integrated into various fields to enhance model performance and extend capabilities to few-shot learning scenarios. We show how meta-learning can significantly improve the accuracy and efficiency of transferring knowledge across domains in domain adaptation. In scenarios involving a multimodal distribution of tasks, we develop methods that efficiently learn from and adapt to a wide variety of tasks drawn from different modes within the distribution, ensuring effective adaptation across diverse domains. Our work on personalized federated learning highlights meta-learning's potential to tailor federated learning processes to individual user needs while maintaining privacy and data security. Additionally, we address the challenges of continual learning by developing models that continuously integrate new information without forgetting previously acquired knowledge. For time series data analysis, we present meta-learning strategies that automatically learn optimal augmentation techniques, enhancing model predictions and offering robust solutions for real-world applications. Lastly, our pioneering research on unsupervised meta-learning via in-context learning explores innovative approaches for constructing tasks and learning effectively from unlabeled data.

Overall, the contributions of this thesis emphasize the potential of meta-learning techniques to improve performance across diverse research areas and demonstrate how advancements in one area can benefit the field as a whole.