Learning Multiple Defaults for Machine Learning Algorithms

v1v2v3 (latest)

Learning Multiple Defaults for Machine Learning Algorithms

23 November 2018

Florian Pfisterer

Jan N. van Rijn

Andreas Müller

ArXiv (abs)PDF HTML

Papers citing "Learning Multiple Defaults for Machine Learning Algorithms"

8 / 8 papers shown

Title
Auto-nnU-Net: Towards Automated Medical Image Segmentation Jannis Becktepe Leona Hennig Steffen Oeltze-Jafra Marius Lindauer 253 0 0 22 May 2025
Multi-Objective Model Selection for Time Series Forecasting Oliver Borchert David Salinas Valentin Flunkert Tim Januschowski Stephan Günnemann AI4TS 74 9 0 17 Feb 2022
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV Katarzyna Wo'znica Mateusz Grzyb Zuzanna Trafas P. Biecek 125 2 0 27 Jan 2022
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 254 514 0 13 Jul 2021
Meta-Learning for Symbolic Hyperparameter Defaults Pieter Gijsbers Florian Pfisterer Jan N. van Rijn B. Bischl Joaquin Vanschoren 72 9 0 10 Jun 2021
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters R. G. Mantovani André Luis Debiaso Rossi Edesio Alcobaça J. C. Gertrudes Sylvio Barbon Junior A. Carvalho 78 11 0 31 Jul 2020
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning Matthias Feurer Katharina Eggensperger Stefan Falkner Marius Lindauer Frank Hutter 133 285 0 08 Jul 2020
Using a thousand optimization tasks to learn hyperparameter search strategies Luke Metz Niru Maheswaranathan Ruoxi Sun C. Freeman Ben Poole Jascha Narain Sohl-Dickstein 119 46 0 27 Feb 2020