HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML

• URL: http://arxiv.org/abs/2106.06257v1
• Date: Fri, 11 Jun 2021 09:18:39 GMT
• Title: HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
• Authors: Sebastian Pineda Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
• Abstract summary: We present HPO-B, a large-scale benchmark for comparing HPO algorithms. Our benchmark is assembled and preprocessed from the OpenML repository. We detail explicit experimental protocols, splits, and evaluation measures for comparing methods for both non-transfer and transfer learning HPO.
• Score: 5.735035463793008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evaluate hyperparameter configurations. As a result, a series of recent related works have focused on the direction of transfer learning for quickly fine-tuning hyperparameters on a dataset. Unfortunately, the community does not have a common large-scale benchmark for comparing HPO algorithms. Instead, the de facto practice consists of empirical protocols on arbitrary small-scale meta-datasets that vary inconsistently across publications, making reproducibility a challenge. To resolve this major bottleneck and enable a fair and fast comparison of black-box HPO methods on a level playing field, we propose HPO-B, a new large-scale benchmark in the form of a collection of meta-datasets. Our benchmark is assembled and preprocessed from the OpenML repository and consists of 176 search spaces (algorithms) evaluated sparsely on 196 datasets with a total of 6.4 million hyperparameter evaluations. For ensuring reproducibility on our benchmark, we detail explicit experimental protocols, splits, and evaluation measures for comparing methods for both non-transfer, as well as, transfer learning HPO.

Related papers

• Rethinking Few-shot 3D Point Cloud Semantic Segmentation [62.80639841429669]
  This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) We focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built.
  arXiv  Detail & Related papers  (2024-03-01T15:14:47Z)
• Large-scale Fully-Unsupervised Re-Identification [78.47108158030213]
  We propose two strategies to learn from large-scale unlabeled data. The first strategy performs a local neighborhood sampling to reduce the dataset size in each without violating neighborhood relationships. A second strategy leverages a novel Re-Ranking technique, which has a lower time upper bound complexity and reduces the memory complexity from O(n2) to O(kn) with k n.
  arXiv  Detail & Related papers  (2023-07-26T16:19:19Z)
• SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization [50.04951511146338]
  Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation for each layer. This paper proposes a novel method for efficiently searching for effective MPQ policies using a small proxy dataset.
  arXiv  Detail & Related papers  (2023-02-14T05:47:45Z)
• Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset [0.15420205433587747]
  We present a two-step HPO method as a strategic solution to curbing computational demands and wait times. We present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation.
  arXiv  Detail & Related papers  (2023-02-08T02:38:26Z)
• Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution [13.037647287689438]
  We study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. We propose a modified HPO method which efficiently searches for optimum global predictive performance. Our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.
  arXiv  Detail & Related papers  (2022-06-11T07:12:04Z)
• FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization [50.12374973760274]
  We propose and implement a benchmark suite FedHPO-B that incorporates comprehensive FL tasks, enables efficient function evaluations, and eases continuing extensions. We also conduct extensive experiments based on FedHPO-B to benchmark a few HPO methods.
  arXiv  Detail & Related papers  (2022-06-08T15:29:10Z)
• Towards Learning Universal Hyperparameter Optimizers with Transformers [57.35920571605559]
  We introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction. Our experiments demonstrate that the OptFormer can imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates.
  arXiv  Detail & Related papers  (2022-05-26T12:51:32Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges [5.139260825952818]
  This paper reviews important HPO methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. It gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with ML pipelines, runtime improvements, and parallelization.
  arXiv  Detail & Related papers  (2021-07-13T04:55:47Z)
• Practical and sample efficient zero-shot HPO [8.41866793161234]
  We provide an overview of available approaches and introduce two novel techniques to handle the problem. The first is based on a surrogate model and adaptively chooses pairs of dataset, configuration to query. The second is for settings where finding, tuning and testing a surrogate model is problematic, is a multi-fidelity technique combining HyperBand with submodular optimization.
  arXiv  Detail & Related papers  (2020-07-27T08:56:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.