Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Modern machine learning requires system designers to specify aspects of the learning
pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn …

While deep learning models have replaced hand-designed features across many domains,
these models are still trained with hand-designed optimizers. In this work, we leverage the …

Deep learning has been successful in automating the design of features in machine learning
pipelines. However, the algorithms optimizing neural network parameters remain largely …

Genetic algorithms constitute a family of black-box optimization algorithms, which take
inspiration from the principles of biological evolution. While they provide a general-purpose …

Unrolled computation graphs arise in many scenarios, including training RNNs, tuning
hyperparameters through unrolled optimization, and training learned optimizers. Current …

Many concepts have been proposed for meta learning with neural networks (NNs), eg, NNs
that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta …