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arXiv:2302.00441 (cs)
[Submitted on 1 Feb 2023 (v1), last revised 25 May 2023 (this
version, v2)]

Title:Power Laws for Hyperparameter Optimization

Authors:Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
Download a PDF of the paper titled Power Laws for Hyperparameter
Optimization, by Arlind Kadra and 3 other authors
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    Abstract: Hyperparameter optimization is an important subfield of
    machine learning that focuses on tuning the hyperparameters of a
    chosen algorithm to achieve peak performance. Recently, there has
    been a stream of methods that tackle the issue of hyperparameter
    optimization, however, most of the methods do not exploit the
    scaling law property of learning curves. In this work, we propose
    Deep Power Laws (DPL), an ensemble of neural network models
    conditioned to yield predictions that follow a power-law scaling
    pattern. Our method dynamically decides which configurations to
    pause and train incrementally by making use of gray-box
    evaluations. We compare our method against 7 state-of-the-art
    competitors on 3 benchmarks related to tabular, image, and NLP
    datasets covering 59 diverse tasks. Our method achieves the best
    results across all benchmarks by obtaining the best any-time
    results compared to all competitors.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2302.00441 [cs.LG]
          (or arXiv:2302.00441v2 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2302.00441
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Arlind Kadra [view email]
[v1] Wed, 1 Feb 2023 13:39:07 UTC (6,818 KB)
[v2] Thu, 25 May 2023 12:35:10 UTC (7,947 KB)
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