| Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in Neural Information Processing Systems, 6845-6855, 2018 | 135 | 2018 |
| A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks J Chan, V Perrone, JP Spence, PA Jenkins, S Mathieson, YS Song Advances in Neural Information Processing Systems, 8603-8614, 2018 | 78 | 2018 |
| Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning V Perrone, H Shen, M Seeger, C Archambeau, R Jenatton Advances in Neural Information Processing Systems, 2019 | 77 | 2019 |
| Poisson Random Fields for Dynamic Feature Models V Perrone, PA Jenkins, D Spano, YW Teh Journal of Machine Learning Research 18 (127), 1-45, 2017 | 56 | 2017 |
| Amazon SageMaker Autopilot: a white box AutoML solution at scale P Das, V Perrone, N Ivkin, T Bansal, Z Karnin, H Shen, I Shcherbatyi, ... Proceedings of the Fourth International Workshop on Data Management for End …, 2020 | 49 | 2020 |
| Fair Bayesian Optimization V Perrone, M Donini, K Kenthapadi, C Archambeau AAAI/ACM Conference on AI, Ethics, and Society (AIES '21), 2020 | 48 | 2020 |
| Relativistic Monte Carlo X Lu*, V Perrone*, L Hasenclever, YW Teh, SJ Vollmer, (*joint first author) Proceedings of the 20th International Conference on Artificial Intelligence …, 2017 | 43 | 2017 |
| A Quantile-based Approach for Hyperparameter Transfer Learning D Salinas, H Shen, V Perrone International Conference on Machine Learning 2020, 7706--7716, 2019 | 40* | 2019 |
| GASC: Genre-Aware Semantic Change for Ancient Greek V Perrone, M Palma, S Hengchen, A Vatri, JQ Smith, B McGillivray ACL International Workshop on Computational Approaches to Historical …, 2019 | 38 | 2019 |
| Cost-aware Bayesian optimization EH Lee, V Perrone, C Archambeau, M Seeger arXiv preprint arXiv:2003.10870, 2020 | 29 | 2020 |
| Constrained Bayesian Optimization with Max-Value Entropy Search V Perrone, I Shcherbatyi, R Jenatton, C Archambeau, M Seeger Advances in Neural Information Processing Systems Workshop on Meta-Learning, 2019 | 28 | 2019 |
| Multiple adaptive Bayesian linear regression for scalable Bayesian optimization with warm start V Perrone, R Jenatton, M Seeger, C Archambeau Advances in Neural Information Processing Systems Workshop on Meta-Learning, 2017 | 24 | 2017 |
| Amazon sagemaker automatic model tuning: Scalable gradient-free optimization V Perrone, H Shen, A Zolic, I Shcherbatyi, A Ahmed, T Bansal, M Donini, ... Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 20* | 2021 |
| Multi-objective multi-fidelity hyperparameter optimization with application to fairness R Schmucker, M Donini, V Perrone, C Archambeau | 15 | 2020 |
| Overfitting in Bayesian Optimization: an empirical study and early-stopping solution A Makarova, H Shen, V Perrone, A Klein, JB Faddoul, A Krause, ... ICLR Workshop on Neural Architecture Search 2021, 2021 | 10 | 2021 |
| Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization G Guinet, V Perrone, C Archambeau arXiv preprint arXiv:2011.11456, 2020 | 9 | 2020 |
| A nonmyopic approach to cost-constrained Bayesian optimization EH Lee, D Eriksson, V Perrone, M Seeger Uncertainty in Artificial Intelligence, 568-577, 2021 | 8 | 2021 |
| Automatic termination for hyperparameter optimization A Makarova, H Shen, V Perrone, A Klein, JB Faddoul, A Krause, ... International Conference on Automated Machine Learning, 7/1-21, 2022 | 6 | 2022 |
| A multi-objective perspective on jointly tuning hardware and hyperparameters D Salinas, V Perrone, O Cruchant, C Archambeau arXiv preprint arXiv:2106.05680, 2021 | 6 | 2021 |
| Flexible and efficient inference with particles for the variational Gaussian approximation T Galy-Fajou, V Perrone, M Opper Entropy 23 (8), 990, 2021 | 5 | 2021 |
Cédric ArchambeauAmazon Web Services, BerlinVerified email at amazon.com
