Automated dynamic algorithm configuration

S Adriaensen, A Biedenkapp, G Shala, N Awad… - Journal of Artificial …, 2022 - jair.org
The performance of an algorithm often critically depends on its parameter configuration.
While a variety of automated algorithm configuration methods have been proposed to …

Masif: Meta-learned algorithm selection using implicit fidelity information

T Ruhkopf, A Mohan, D Deng, A Tornede… - … on Machine Learning …, 2022 - openreview.net
Selecting a well-performing algorithm for a given task or dataset can be time-consuming and
tedious, but is crucial for the successful day-to-day business of developing new AI & ML …

Meta-learning from learning curves for budget-limited algorithm selection

MH Nguyen, LS Hosoya, I Guyon - Pattern Recognition Letters, 2024 - Elsevier
Training a large set of machine learning algorithms to convergence in order to select the
best-performing algorithm for a dataset is computationally wasteful. Moreover, in a budget …

Towards meta-learned algorithm selection using implicit fidelity information

A Mohan, T Ruhkopf, M Lindauer - arXiv preprint arXiv:2206.03130, 2022 - arxiv.org
Automatically selecting the best performing algorithm for a given dataset or ranking multiple
algorithms by their expected performance supports users in developing new machine …

Meta-learning from Learning Curves: Challenge Design and Baseline Results

MH Nguyen, L Sun-Hosoya… - … Joint Conference on …, 2022 - ieeexplore.ieee.org
Meta-Iearning has been widely studied and implemented in many Automated Machine
Learning systems to improve the process of selecting and training Machine Learning models …

Reinforcement learning for combinatorial optimization: leveraging uncertainty, structure and priors

N Grinsztajn - 2023 - theses.hal.science
Combinatorial optimization problems have been extensively studied due to their numerous
applications (planning, logistics, distribution, investment, production...) and their complexity …