Michele Donini
Michele Donini
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Empirical risk minimization under fairness constraints
M Donini, L Oneto, S Ben-David, J Shawe-Taylor, M Pontil
arXiv preprint arXiv:1802.08626, 2018
Forward and reverse gradient-based hyperparameter optimization
L Franceschi, M Donini, P Frasconi, M Pontil
International Conference on Machine Learning, 1165-1173, 2017
EasyMKL: a scalable multiple kernel learning algorithm
F Aiolli, M Donini
Neurocomputing 169, 215-224, 2015
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
Learning deep kernels in the space of dot product polynomials
M Donini, F Aiolli
Machine Learning 106 (9), 1245-1269, 2017
General fair empirical risk minimization
L Oneto, M Donini, M Pontil
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
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 (6), 1015-1033, 2016
Multiple graph-kernel learning
F Aiolli, M Donini, N Navarin, A Sperduti
2015 IEEE Symposium Series on Computational Intelligence, 1607-1614, 2015
ClimbTheWorld: Real-time stairstep counting to increase physical activity
F Aiolli, M Ciman, M Donini, O Gaggi
Proceedings of the 11th International Conference on Mobile and Ubiquitous …, 2014
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
Scuba: scalable kernel-based gene prioritization
G Zampieri, D Van Tran, M Donini, N Navarin, F Aiolli, A Sperduti, G Valle
BMC bioinformatics 19 (1), 1-12, 2018
Measuring the expressivity of graph kernels through statistical learning theory
L Oneto, N Navarin, M Donini, A Sperduti, F Aiolli, D Anguita
Neurocomputing 268, 4-16, 2017
Learning fair and transferable representations
L Oneto, M Donini, A Maurer, M Pontil
arXiv preprint arXiv:1906.10673, 2019
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning.
L Oneto, M Donini, G Luise, C Ciliberto, A Maurer, M Pontil
NeurIPS, 2020
A multimodal multiple kernel learning approach to Alzheimer's disease detection
M Donini, JM Monteiro, M Pontil, J Shawe-Taylor, J Mourao-Miranda
2016 IEEE 26th International Workshop on Machine Learning for Signal …, 2016
Learning Anisotropic RBF Kernels
F Aiolli, M Donini
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important
M Donini, JM Monteiro, M Pontil, T Hahn, AJ Fallgatter, J Shawe-Taylor, ...
NeuroImage 195, 215-231, 2019
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
V Perrone, H Shen, A Zolic, I Shcherbatyi, A Ahmed, T Bansal, M Donini, ...
A bridge between hyperparameter optimization and larning-to-learn
L Franceschi, P Frasconi, M Donini, M Pontil
stat 1050, 18, 2017
An efficient method to impose fairness in linear models
M Donini, S Ben-David, M Pontil, J Shawe-Taylor
NIPS workshop on prioritising online content, 2017
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Artiklar 1–20