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Learning curve prediction for AutoML

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Automated machine learning (AutoML) aims to automate the process of selecting hyper-parameters for machine learning models, such as learning rate, batch size, or layer width. To this end, machine learning models are trained with different hyper-parameter configurations, their final performance is recorded, and new candidate configurations are selected via Bayesian optimisation. The latter typically constructs a probabilistic surrogate of final model performances as a function of hyper-parameter configurations. However, individual training runs are usually subject to intermediate evaluations, which produce learning curves in addition to their final performance. These learning curves could be leveraged to 1) save resources by stopping unpromising runs early, and 2) improve the probabilistic surrogate to select better candidate configurations. This reading group will review the literature on building scalable probabilistic surrogate models of such learning curves, discussing approaches using Gaussian processes, power laws, Bayesian neural networks, and Transformers.

This talk is part of the Machine Learning Reading Group @ CUED series.

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