@inproceedings{salinas-icml23,title={Optimizing Hyperparameters with Conformal Quantile Regression},author={Salinas, D. and Golebiowsk, J. and Klein, A. and Seeger, M. and Archambeau, C.},booktitle={to appear at the International Conference on Machine Learning (ICML'23)},year={2023},pdf={conformal_hpo.pdf},bibtex_show={true},code={https://github.com/awslabs/syne-tune}}
2022
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
Salinas, D., Seeger, M., Klein, A., Perrone, V., Wistuba, M., and Archambeau, C.
In First Conference on Automated Machine Learning (Main Track) 2022
@inproceedings{salinas-automl22,title={Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research},author={Salinas, D. and Seeger, M. and Klein, A. and Perrone, V. and Wistuba, M. and Archambeau, C.},booktitle={First Conference on Automated Machine Learning (Main Track)},year={2022},pdf={syne_tune.pdf},bibtex_show={true},code={https://github.com/awslabs/syne-tune}}
Automatic Termination for Hyperparameter Optimization
Makarova, A., Shen, H., Perrone, V., Klein, A., Faddoul, J. B., Krause, A., Seeger, M., and Archambeau, C.
In First Conference on Automated Machine Learning (Main Track) 2022
@inproceedings{makarova-automl22,title={Automatic Termination for Hyperparameter Optimization},author={Makarova, A. and Shen, H. and Perrone, V. and Klein, A. and Faddoul, J. B. and Krause, A. and Seeger, M. and Archambeau, C.},booktitle={First Conference on Automated Machine Learning (Main Track)},year={2022},pdf={termination_criterion.pdf},bibtex_show={true},code={https://github.com/amazon-science/bo-early-stopping}}
2021
BORE: Bayesian Optimization by Density-Ratio Estimation
Tiao, L., Klein, A., Seeger, M., Bonilla, E., Archambeau, C., and Ramos, F.
In Proceedings of the 38th International Conference on Machine Learning (ICML’21) 2021
@inproceedings{tiao-icml21,title={BORE: Bayesian Optimization by Density-Ratio Estimation},author={Tiao, L. and Klein, A. and Seeger, M. and Bonilla, E. and Archambeau, C. and Ramos, F.},booktitle={Proceedings of the 38th International Conference on Machine Learning (ICML'21)},year={2021},pdf={bore.pdf},bibtex_show={true},code={https://github.com/awslabs/syne-tune}}
Hyperparameter Transfer Learning with Adaptive Complexity
Horváth, S., Klein, A., Richtarik, P., and Archambeau, C.
In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS’21) 2021
@inproceedings{horvath-aistats21,title={Hyperparameter Transfer Learning with Adaptive Complexity },author={Horv{\'a}th, S. and Klein, A. and Richtarik, P. and Archambeau, C.},booktitle={Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS'21)},year={2021},pdf={abrac.pdf},bibtex_show={true}}
2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Klein, A., Dai, Z., Hutter, F., Lawrence, N., and Gonzalez, J.
In Proceedings of the 32th International Conference on Advances in Neural Information Processing Systems (NIPS’19) 2019
@inproceedings{klein-nips19,title={Meta-Surrogate Benchmarking for Hyperparameter Optimization},author={Klein, A. and Dai, Z. and Hutter, F. and Lawrence, N. and Gonzalez, J.},booktitle={Proceedings of the 32th International Conference on Advances in Neural Information Processing Systems (NIPS'19)},year={2019},pdf={profet.pdf},bibtex_show={true},code={https://github.com/EmuKit/emukit/tree/main/emukit/examples/profet}}
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., and Hutter, F.
In Proceedings of the 36th International Conference on Machine Learning (ICML’19) 2019
@inproceedings{ying-icml19,author={Ying, C. and Klein, A. and Real, E. and Christiansen, E. and Murphy, K. and Hutter, F.},title={{NAS-Bench-101}: Towards Reproducible Neural Architecture Search},booktitle={Proceedings of the 36th International Conference on Machine Learning (ICML'19)},year={2019},pdf={nasbench101.pdf},bibtex_show={true},code={https://github.com/google-research/nasbench}}
2018
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Falkner, S., Klein, A., and Hutter, F.
In Proceedings of the 35th International Conference on Machine Learning (ICML#18) 2018
@inproceedings{falkner-icml18,title={{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},author={Falkner, S. and Klein, A. and Hutter, F.},booktitle={Proceedings of the 35th International Conference on Machine Learning (ICML#18)},year={2018},pdf={bohb.pdf},bibtex_show={true},code={https://github.com/automl/HpBandSter}}
2017
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F.
In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS’17) 2017
@inproceedings{klein-aistats17,author={Klein, A. and Falkner, S. and Bartels, S. and Hennig, P. and Hutter, F.},title={Fast {Bayesian} Optimization of Machine Learning Hyperparameters on Large Datasets},booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'17)},year={2017},pdf={fabolas.pdf},bibtex_show={true},code={https://github.com/EmuKit/emukit/tree/main/emukit/examples/fabolas}}
2016
Bayesian Optimization with Robust Bayesian Neural Networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F.
In Proceedings of the 29th International Conference on Advances in Neural Information Processing Systems (NIPS’16) 2016
@inproceedings{springenberg-nips16,title={Bayesian Optimization with Robust Bayesian Neural Networks},author={Springenberg, J. T. and Klein, A. and Falkner, S. and Hutter, F.},booktitle={Proceedings of the 29th International Conference on Advances in Neural Information Processing Systems (NIPS'16)},year={2016},pdf={bohamiann.pdf},bibtex_show={true},code={https://github.com/automl/pybnn}}
2015
Efficient and Robust Automated Machine Learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F.
In Proceedings of the 28th International Conference on Advances in Neural Information Processing Systems (NIPS’15) 2015
@inproceedings{feurer-nips2015,booktitle={Proceedings of the 28th International Conference on Advances in Neural Information Processing Systems (NIPS'15)},title={Efficient and Robust Automated Machine Learning},author={Feurer, M. and Klein, A. and Eggensperger, K. and Springenberg, J. and Blum, M. and Hutter, F.},year={2015},pdf={autosklearn.pdf},bibtex_show={true},code={https://github.com/automl/auto-sklearn}}