Michele Donini

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 59

Top Topics

Empirical Risk Minimization

Kernel Learning

Ensemble Classifier

Top Venues

Publications

Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
Explaining Probabilistic Models with Distributional Values. CoRR (2024)
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 (2024)
Todd W. Neller, Raechel Walker, Olivia Dias, Zeynep Yalçin, Cynthia Breazeal, Matt 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)
Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
Efficient fair PCA for fair representation learning. AISTATS (2023)
Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
Efficient fair PCA for fair representation learning. CoRR (2023)
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)
Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
Geographical Erasure in Language Generation. EMNLP (Findings) (2023)
Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR (2023)
Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
Geographical Erasure in Language Generation. CoRR (2023)
Danilo Franco, Nicolò Navarin, Michele Donini, Davide Anguita, Luca Oneto
Deep fair models for complex data: Graphs labeling and explainable face recognition. Neurocomputing 470 (2022)
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)
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 (2022)
Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis. AAMAS (2022)
Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR (2021)
Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit A. 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 (2021)
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)
Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit A. 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)
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
Multi-objective Asynchronous Successive Halving. CoRR (2021)
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 (2021)
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
Fair Bayesian Optimization. AIES (2021)
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) (2021)
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)
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 (2021)
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 (2020)
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)
Luca Oneto, Michele Donini, Massimiliano Pontil
General Fair Empirical Risk Minimization. IJCNN (2020)
Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi
Marthe: Scheduling the Learning Rate Via Online Hypergradients. IJCAI (2020)
Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer
Learning Fair and Transferable Representations with Theoretical Guarantees. DSAA (2020)
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 (2020)
Luca Oneto, Nicolò Navarin, Michele Donini
Learning Deep Fair Graph Neural Networks. ESANN (2020)
Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau
Fair Bayesian Optimization. CoRR (2020)
Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
Voting with Random Classifiers (VORACE). AAMAS (2020)
Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
Taking Advantage of Multitask Learning for Fair Classification. AIES (2019)
Luca Oneto, Michele Donini, Massimiliano Pontil
General Fair Empirical Risk Minimization. CoRR (2019)
Luca Oneto, Michele Donini, Massimiliano Pontil
PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. ESANN (2019)
Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
Voting with Random Classifiers (VORACE). CoRR (2019)
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 (2019)
Luca Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil
Learning Fair and Transferable Representations. CoRR (2019)
Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi
Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. CoRR (2019)
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) (2018)
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
Empirical Risk Minimization Under Fairness Constraints. NeurIPS (2018)
Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
Taking Advantage of Multitask Learning for Fair Classification. CoRR (2018)
Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
Voting with Random Neural Networks: a Democratic Ensemble Classifier. RiCeRcA@AI*IA (2018)
Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita
Emerging trends in machine learning: beyond conventional methods and data. ESANN (2018)
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) (2018)
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
Empirical Risk Minimization under Fairness Constraints. CoRR (2018)
Ivano Lauriola, Michele Donini, Fabio Aiolli
Learning dot-product polynomials for multiclass problems. ESANN (2017)
Michele Donini, Fabio Aiolli
Learning deep kernels in the space of dot product polynomials. Mach. Learn. 106 (9-10) (2017)
Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
Forward and Reverse Gradient-Based Hyperparameter Optimization. ICML (2017)
Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
A Bridge Between Hyperparameter Optimization and Larning-to-learn. CoRR (2017)
Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing 268 (2017)
Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN (2017)
Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil
A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. INTERSPEECH (2017)
Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
On Hyperparameter Optimization in Learning Systems. ICLR (Workshop) (2017)
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)
Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN (2016)
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)
Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil
Distributed variance regularized Multitask Learning. IJCNN (2016)
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) (2016)
Fabio Aiolli, Michele Donini
EasyMKL: a scalable multiple kernel learning algorithm. Neurocomputing 169 (2015)
Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti
Multiple Graph-Kernel Learning. SSCI (2015)
Verónica Bolón-Canedo, Michele Donini, Fabio Aiolli
Feature and kernel learning. ESANN (2015)
Fabio Aiolli, Michele Donini
Learning Anisotropic RBF Kernels. ICANN (2014)
Fabio Aiolli, Michele Donini
Easy multiple kernel learning. ESANN (2014)
Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi
ClimbTheWorld: real-time stairstep counting to increase physical activity. MobiQuitous (2014)