AutoML 2022 Track Journal Submissions

HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Alexander Imani Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan-Rhys Griffiths, Alexandre Max Maraval, Jianye HAO, Jun Wang, Jan Peters, Haitham Bou Ammar
- Published: 17 May 2023, Last Modified: 17 May 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone
Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?
Antoine Grosnit, Alexander Imani Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou Ammar
- Published: 17 May 2023, Last Modified: 17 May 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone
Hyperparameter Optimization via Sequential Uniform Designs
Zebin YANG, Aijun Zhang
- Published: 17 May 2023, Last Modified: 22 Oct 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone
Bayesian optimization for policy search via online-offline experimentation
Benjamin Letham, Eytan Bakshy
- Published: 17 May 2023, Last Modified: 17 May 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone
Model Averaging Is Asymptotically Better Than Model Selection For Prediction
Tri M. Le
- Published: 17 May 2023, Last Modified: 17 May 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone
MFE: Towards reproducible meta-feature extraction
Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luis Paulo Faina Garcia, Jefferson T. Oliva, Andre Carlos Ponce de Leon Ferreira De Carvalho
- Published: 17 May 2023, Last Modified: 17 May 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone
Algorithm selection on a meta level
Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier
- Published: 17 May 2023, Last Modified: 22 Oct 2023
- AutoML-Conf 2022 (Journal Track)
- Readers: Everyone