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Journal Articles
- 2024
[j11]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. 25: 238:1-238:7 (2024)- 2022
- [j10]Danilo Franco
, Nicolò Navarin, Michele Donini, Davide Anguita, Luca Oneto:
Deep fair models for complex data: Graphs labeling and explainable face recognition. Neurocomputing 470: 318-334 (2022) - 2021
- [j9]Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi:
Voting with random classifiers (VORACE): theoretical and experimental analysis. Auton. Agents Multi Agent Syst. 35(2): 22 (2021) - 2020
- [j8]Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor:
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing 416: 231-243 (2020) - 2019
- [j7]Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda:
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage 195: 215-231 (2019) - 2018
- [j6]Guido Zampieri, Dinh Tran-Van, Michele Donini, Nicolò Navarin, Fabio Aiolli, Alessandro Sperduti, Giorgio Valle:
Scuba: scalable kernel-based gene prioritization. BMC Bioinform. 19(1): 23:1-23:12 (2018) - [j5]Luca Oneto, Nicolò Navarin, Michele Donini, Sandro Ridella, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4660-4671 (2018) - 2017
- [j4]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing 268: 4-16 (2017) - [j3]Michele Donini, Fabio Aiolli:
Learning deep kernels in the space of dot product polynomials. Mach. Learn. 106(9-10): 1245-1269 (2017) - 2016
- [j2]Matteo Ciman, Michele Donini, Ombretta Gaggi, Fabio Aiolli:
Stairstep recognition and counting in a serious Game for increasing users' physical activity. Pers. Ubiquitous Comput. 20(6): 1015-1033 (2016) - 2015
- [j1]Fabio Aiolli, Michele Donini:
EasyMKL: a scalable multiple kernel learning algorithm. Neurocomputing 169: 215-224 (2015)
Conference and Workshop Papers
- 2024
- [c36]Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger:
Explaining Probabilistic Models with Distributional Values. ICML 2024 - 2023
- [c35]Todd W. Neller, Raechel Walker, Olivia Dias, Zeynep Yalçin, Cynthia Breazeal, Matthew E. Taylor, Michele Donini, Erin J. Talvitie, Charlie Pilgrim, Paolo Turrini, James Maher, Matthew Boutell, Justin Wilson, Narges Norouzi, Jonathan Scott:
Model AI Assignments 2023. AAAI 2023: 16104-16105 - [c34]Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar:
Efficient fair PCA for fair representation learning. AISTATS 2023: 5250-5270 - [c33]Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi:
Geographical Erasure in Language Generation. EMNLP (Findings) 2023: 12310-12324 - [c32]Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger:
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H 2023: 11-24 - 2022
- [c31]Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi:
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis. AAMAS 2022: 1929-1931 - [c30]David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi:
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. KDD 2022: 3671-3681 - 2021
- [c29]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) 2021: 3730-3740 - [c28]Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. AIES 2021: 854-863 - [c27]Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. KDD 2021: 2974-2983 - [c26]Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau:
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD 2021: 3463-3471 - 2020
- [c25]Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi:
Voting with Random Classifiers (VORACE). AAMAS 2020: 1822-1824 - [c24]Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer:
Learning Fair and Transferable Representations with Theoretical Guarantees. DSAA 2020: 30-39 - [c23]Luca Oneto, Nicolò Navarin, Michele Donini:
Learning Deep Fair Graph Neural Networks. ESANN 2020: 31-36 - [c22]Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi:
Marthe: Scheduling the Learning Rate Via Online Hypergradients. IJCAI 2020: 2119-2125 - [c21]Luca Oneto, Michele Donini, Massimiliano Pontil:
General Fair Empirical Risk Minimization. IJCNN 2020: 1-8 - [c20]Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil:
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. NeurIPS 2020 - 2019
- [c19]Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil:
Taking Advantage of Multitask Learning for Fair Classification. AIES 2019: 227-237 - [c18]Luca Oneto, Michele Donini, Massimiliano Pontil:
PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. ESANN 2019 - 2018
- [c17]Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi:
Voting with Random Neural Networks: a Democratic Ensemble Classifier. RiCeRcA@AI*IA 2018 - [c16]Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita:
Emerging trends in machine learning: beyond conventional methods and data. ESANN 2018 - [c15]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization Under Fairness Constraints. NeurIPS 2018: 2796-2806 - 2017
- [c14]Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa:
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN 2017 - [c13]Ivano Lauriola, Michele Donini, Fabio Aiolli:
Learning dot-product polynomials for multiclass problems. ESANN 2017 - [c12]Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
On Hyperparameter Optimization in Learning Systems. ICLR (Workshop) 2017 - [c11]Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
Forward and Reverse Gradient-Based Hyperparameter Optimization. ICML 2017: 1165-1173 - [c10]Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil:
A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. INTERSPEECH 2017: 984-988 - 2016
- [c9]Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita:
Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. ESANN 2016 - [c8]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN 2016 - [c7]Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil:
Distributed variance regularized Multitask Learning. IJCNN 2016: 3101-3109 - [c6]Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda:
A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP 2016: 1-6 - 2015
- [c5]Verónica Bolón-Canedo, Michele Donini, Fabio Aiolli:
Feature and kernel learning. ESANN 2015 - [c4]Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti:
Multiple Graph-Kernel Learning. SSCI 2015: 1607-1614 - 2014
- [c3]Fabio Aiolli, Michele Donini:
Easy multiple kernel learning. ESANN 2014 - [c2]Fabio Aiolli, Michele Donini:
Learning Anisotropic RBF Kernels. ICANN 2014: 515-522 - [c1]Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi:
ClimbTheWorld: real-time stairstep counting to increase physical activity. MobiQuitous 2014: 218-227
Informal and Other Publications
- 2024
- [i20]Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger:
Explaining Probabilistic Models with Distributional Values. CoRR abs/2402.09947 (2024) - [i19]Pola Schwöbel, Luca Franceschi, Muhammad Bilal Zafar, Keerthan Vasist, Aman Malhotra, Tomer Shenhar, Pinal Tailor, Pinar Yilmaz, Michael Diamond, Michele Donini:
Evaluating Large Language Models with fmeval. CoRR abs/2407.12872 (2024) - 2023
- [i18]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR abs/2302.04019 (2023) - [i17]Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar:
Efficient fair PCA for fair representation learning. CoRR abs/2302.13319 (2023) - [i16]Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi:
Geographical Erasure in Language Generation. CoRR abs/2310.14777 (2023) - 2022
- [i15]Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau:
Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR abs/2203.11103 (2022) - 2021
- [i14]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR abs/2106.04631 (2021) - [i13]Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau:
Multi-objective Asynchronous Successive Halving. CoRR abs/2106.12639 (2021) - [i12]Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. CoRR abs/2109.03285 (2021) - [i11]David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi:
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. CoRR abs/2111.13657 (2021) - [i10]Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi:
More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR abs/2112.12444 (2021) - 2020
- [i9]Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. CoRR abs/2006.05109 (2020) - [i8]Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau:
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR abs/2012.08489 (2020) - 2019
- [i7]Luca Oneto, Michele Donini, Massimiliano Pontil:
General Fair Empirical Risk Minimization. CoRR abs/1901.10080 (2019) - [i6]Luca Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil:
Learning Fair and Transferable Representations. CoRR abs/1906.10673 (2019) - [i5]Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi:
Voting with Random Classifiers (VORACE). CoRR abs/1909.08996 (2019) - [i4]Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi:
Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. CoRR abs/1910.08525 (2019) - 2018
- [i3]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization under Fairness Constraints. CoRR abs/1802.08626 (2018) - [i2]Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil:
Taking Advantage of Multitask Learning for Fair Classification. CoRR abs/1810.08683 (2018) - 2017
- [i1]Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
A Bridge Between Hyperparameter Optimization and Larning-to-learn. CoRR abs/1712.06283 (2017)
Coauthor Index
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