The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new algorithm. Those default values are usually chosen in an ad-hoc manner to work good enough on a wide variety of datasets. To address this problem, different automatic hyperparameter configuration algorithms have been proposed, which select an optimal configuration per dataset. This principled approach usually improves performance but adds additional algorithmic complexity and computational costs to the training procedure. As an alternative to this, we propose learning a set of complementary default values from a large database of prior empirical results. Selecting an appropriate configuration on a new dataset then requires only a simple, efficient and embarrassingly parallel search over this set. We demonstrate the effectiveness and efficiency of the approach we propose in comparison to random search and Bayesian Optimization.
Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
Osnabrück University, Joint Lab Artificial Intelligence and Data Science（奥尔登堡大学，人工智能与数据科学联合实验室）；Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB)（莱比锡农业工程与生物经济研究所（ATB））；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Benchmarking Machine Learning Models to Predict Corporate Bankruptcy
McCoy College of Business at Texas State University（德克萨斯州立大学麦考伊商学院）；Scheller College of Business at Georgia Institute of Technology（佐治亚理工学院谢勒商学院）；College of Computing at Georgia Institute of Technology（佐治亚理工学院计算学院）
mlf-core: a framework for deterministic machine learning
University of Tübingen（图宾根大学）；Helmholtz Zentrum München（亥姆霍兹慕尼黑中心）；Technical University of Munich（慕尼黑工业大学）；University of Hamburg（汉堡大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Federal Technology University - Paraná（巴拉那联邦技术大学）；São Paulo State University (UNESP)（圣保罗州立大学）；University of São Paulo (USP)（圣保罗大学）；Federal University of Ouro Preto (UFOP)（欧鲁普雷图联邦大学）；State University of Londrina (UEL)（隆德里纳州立大学）
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
Federal Technology University（联邦技术大学）；University of São Paulo（圣保罗大学）；Universidade Estadual Paulista（保利斯塔州立大学）；Eindhoven University of Technology（埃因霍温理工大学）
