| Empirical risk minimization under fairness constraints M Donini, L Oneto, S Ben-David, JS Shawe-Taylor, M Pontil Advances in neural information processing systems 31, 2018 | 553 | 2018 |
| Forward and reverse gradient-based hyperparameter optimization L Franceschi, M Donini, P Frasconi, M Pontil International Conference on Machine Learning, 1165-1173, 2017 | 527 | 2017 |
| EasyMKL: a scalable multiple kernel learning algorithm F Aiolli, M Donini Neurocomputing 169, 215-224, 2015 | 202 | 2015 |
| Fair bayesian optimization V Perrone, M Donini, MB Zafar, R Schmucker, K Kenthapadi, ... Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 854-863, 2021 | 93 | 2021 |
| Taking advantage of multitask learning for fair classification L Oneto, M Doninini, A Elders, M Pontil Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 227-237, 2019 | 87 | 2019 |
| Exploiting mmd and sinkhorn divergences for fair and transferable representation learning L Oneto, M Donini, G Luise, C Ciliberto, A Maurer, M Pontil Advances in Neural Information Processing Systems 33, 15360-15370, 2020 | 63 | 2020 |
| General fair empirical risk minimization L Oneto, M Donini, M Pontil 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 56 | 2020 |
| Amazon sagemaker clarify: Machine learning bias detection and explainability in the cloud M Hardt, X Chen, X Cheng, M Donini, J Gelman, S Gollaprolu, J He, ... Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 55 | 2021 |
| Amazon sagemaker model monitor: A system for real-time insights into deployed machine learning models D Nigenda, Z Karnin, MB Zafar, R Ramesha, A Tan, M Donini, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 48 | 2022 |
| Learning fair and transferable representations with theoretical guarantees L Oneto, M Donini, M Pontil, A Maurer 2020 IEEE 7th International Conference on Data Science and Advanced …, 2020 | 47 | 2020 |
| Fairness measures for machine learning in finance S Das, M Donini, J Gelman, K Haas, M Hardt, J Katzman, K Kenthapadi, ... | 38 | 2021 |
| Learning deep kernels in the space of dot product polynomials M Donini, F Aiolli Machine Learning 106, 1245-1269, 2017 | 38 | 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 | 37* | 2021 |
| Deep fair models for complex data: Graphs labeling and explainable face recognition D Franco, N Navarin, M Donini, D Anguita, L Oneto Neurocomputing 470, 318-334, 2022 | 35 | 2022 |
| Voting with random classifiers (VORACE): theoretical and experimental analysis C Cornelio, M Donini, A Loreggia, MS Pini, F Rossi Autonomous Agents and Multi-Agent Systems 35 (2), 22, 2021 | 34 | 2021 |
| Stairstep recognition and counting in a serious Game for increasing users’ physical activity M Ciman, M Donini, O Gaggi, F Aiolli Personal and Ubiquitous Computing 20, 1015-1033, 2016 | 31 | 2016 |
| Multi-objective multi-fidelity hyperparameter optimization with application to fairness R Schmucker, M Donini, V Perrone, C Archambeau | 29 | 2020 |
| Multiple graph-kernel learning F Aiolli, M Donini, N Navarin, A Sperduti 2015 IEEE Symposium Series on Computational Intelligence, 1607-1614, 2015 | 29 | 2015 |
| Scuba: scalable kernel-based gene prioritization G Zampieri, DV Tran, M Donini, N Navarin, F Aiolli, A Sperduti, G Valle BMC bioinformatics 19, 1-12, 2018 | 26 | 2018 |
| Multi-objective asynchronous successive halving R Schmucker, M Donini, MB Zafar, D Salinas, C Archambeau arXiv preprint arXiv:2106.12639, 2021 | 25 | 2021 |
Massimiliano PontilIstituto Italiano di Tecnologia & University College Londoniit.it의 이메일 확인됨