Learning curves are a concept from social sciences that has been adopted in the context of
machine learning to assess the performance of a learning algorithm with respect to a certain …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

Learning curve extrapolation aims to predict model performance in later epochs of training,
based on the performance in earlier epochs. In this work, we argue that, while the inherent …

Algorithm selection aims to identify the most suitable algorithm for solving a specific problem
before execution, which has become a critical process of the AutoML. Current mainstream …

Algorithm selection, a critical process of automated machine learning, aims to identify the
most suitable algorithm for solving a specific problem prior to execution. Mainstream …

With the increasing computational costs associated with deep learning, automated
hyperparameter optimization methods, strongly relying on black-box Bayesian optimization …

To reach high performance with deep learning, hyperparameter optimization (HPO) is
essential. This process is usually time-consuming due to costly evaluations of neural …

In the algorithm selection research, the discussion surrounding algorithm features has been
significantly overshadowed by the emphasis on problem features. Although a few empirical …

Training a large set of machine learning algorithms to convergence in order to select the
best-performing algorithm for a dataset is computationally wasteful. Moreover, in a budget …

Learning curves depict how a model's expected performance changes with varying training
set sizes, unlike training curves, showing a gradient-based model's performance with …