ML Anthology

Adriaensen, Steven

10 publications

ICML 2025 Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Dongwoo Lee, Dong Bok Lee, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Frank Hutter, Seon Joo Kim, Hae Beom Lee
NeurIPS 2025 Cost-Sensitive Freeze-Thaw Bayesian Optimization for Efficient Hyperparameter Tuning Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Hae Beom Lee
NeurIPS 2025 Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics Indrashis Das, Mahmoud Safari, Steven Adriaensen, Frank Hutter
ICLRW 2025 Α-PFN: In-Context Learning Entropy Search Tom Julian Viering, Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Carl Hvarfner, Frank Hutter, Eytan Bakshy
ICML 2024 In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter
NeurIPS 2023 Efficient Bayesian Learning Curve Extrapolation Using Prior-Data Fitted Networks Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter
JAIR 2022 Automated Dynamic Algorithm Configuration Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
NeurIPSW 2022 Efficient Bayesian Learning Curve Extrapolation Using Prior-Data Fitted Networks Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter
IJCAI 2021 DACBench: A Benchmark Library for Dynamic Algorithm Configuration Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
IJCAI 2016 Towards a White Box Approach to Automated Algorithm Design Steven Adriaensen, Ann Nowé