The field of machine learning is witnessing a significant shift towards the development of more explainable and efficient models. Recent research has focused on integrating active learning, meta-learning, and explainable AI techniques to optimize complex systems and improve model performance. This has led to the creation of novel frameworks that can efficiently identify promising solutions, provide interpretable insights, and facilitate expert-driven analysis. Notably, the use of meta-learning and explainable AI has shown great promise in hyperparameter optimization, model selection, and pipeline development.

Some noteworthy papers in this area include: MetaLLMix, which proposes a zero-shot hyperparameter optimization framework that combines meta-learning and explainable AI to achieve competitive performance while reducing computational cost. PIPES, a comprehensive meta-dataset of machine learning pipelines that provides a diverse and representative collection of experiments, allowing researchers to perform analyses across various pipelines and datasets. Balancing Sparse RNNs with Hyperparameterization Benefiting Meta-Learning, which develops alternative hyperparameters for specifying sparse Recurrent Neural Networks and enables significant performance gains.