| Searching molecular structure databases with tandem mass spectra using CSI: FingerID K Dührkop, H Shen, M Meusel, J Rousu, S Böcker Proceedings of the National Academy of Sciences 112 (41), 12580-12585, 2015 | 852 | 2015 |
| Metabolite identification and molecular fingerprint prediction through machine learning M Heinonen, H Shen, N Zamboni, J Rousu Bioinformatics 28 (18), 2333-2341, 2012 | 199 | 2012 |
| Critical assessment of small molecule identification 2016: automated methods EL Schymanski, C Ruttkies, M Krauss, C Brouard, T Kind, K Dührkop, ... Journal of cheminformatics 9, 1-21, 2017 | 184 | 2017 |
| Metabolite identification through multiple kernel learning on fragmentation trees H Shen, K Dührkop, S Böcker, J Rousu Bioinformatics 30 (12), i157-i164, 2014 | 122 | 2014 |
| Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning V Perrone, H Shen, MW Seeger, C Archambeau, R Jenatton Advances in Neural Information Processing Systems 32, 2019 | 105 | 2019 |
| Fast metabolite identification with input output kernel regression C Brouard, H Shen, K Dührkop, F d'Alché-Buc, S Böcker, J Rousu Bioinformatics 32 (12), i28-i36, 2016 | 98 | 2016 |
| Amazon SageMaker Autopilot: a white box AutoML solution at scale P Das, N Ivkin, T Bansal, L Rouesnel, P Gautier, Z Karnin, L Dirac, ... Proceedings of the fourth international workshop on data management for end …, 2020 | 76 | 2020 |
| A quantile-based approach to hyperparameter transfer learning D Salinas, H Shen, V Perrone Advances in Neural Information Processing Systems Workshop on Meta-Learning, 2019 | 48 | 2019 |
| Metabolite identification through machine learning—tackling CASMI challenge using FingerID H Shen, N Zamboni, M Heinonen, J Rousu Metabolites 3 (2), 484-505, 2013 | 37 | 2013 |
| 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 | 22 | 2022 |
| 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 | 19 | 2021 |
| Amazon SageMaker automatic model tuning: Scalable black-box optimization V Perrone, H Shen, A Zolic, I Shcherbatyi, A Ahmed, T Bansal, M Donini, ... arXiv preprint arXiv:2012.08489 1, 2020 | 14 | 2020 |
| Overfitting in Bayesian optimization: an empirical study and early-stopping solution A Makarova, H Shen, V Perrone, A Klein, JB Faddoul, A Krause, ... 2nd Workshop on Neural Architecture Search (NAS 2021 collocated with the 9th …, 2021 | 13 | 2021 |
| AutoGluon–TimeSeries: AutoML for probabilistic time series forecasting O Shchur, AC Turkmen, N Erickson, H Shen, A Shirkov, T Hu, B Wang International Conference on Automated Machine Learning, 9/1-21, 2023 | 6 | 2023 |
| A copula approach for hyperparameter transfer learning D Salinas, H Shen, V Perrone | 5 | 2019 |
| Soft kernel target alignment for two-stage multiple kernel learning H Shen, S Szedmak, C Brouard, J Rousu Discovery Science: 19th International Conference, DS 2016, Bari, Italy …, 2016 | 4 | 2016 |
| Obeying the order: Introducing ordered transfer hyperparameter optimisation SP Hellan, H Shen, FX Aubet, D Salinas, A Klein arXiv preprint arXiv:2306.16916, 2023 | 3 | 2023 |
| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 2 | 2024 |
| Supplementary Material: A Quantile-based Approach for Hyperparameter Transfer Learning D Salinas, H Shen, V Perrone | 1 | |
| Cross-Frequency Time Series Meta-Forecasting M Van Ness, H Shen, H Wang, X Jin, DC Maddix, K Gopalswamy arXiv preprint arXiv:2302.02077, 2023 | | 2023 |
Juho RousuProfessor, Department of Computer Science, Aalto UniversityVerified email at aalto.fi
