Michele Donini

According to our database, Michele Donini authored at least 56 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Fortuna: A Library for Uncertainty Quantification in Deep Learning.

Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau

J. Mach. Learn. Res., 2024

Hyperparameter Optimization in Machine Learning.

Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi

CoRR, 2024

Evaluating Large Language Models with fmeval.

Pola Schwöbel, Luca Franceschi, Muhammad Bilal Zafar, Keerthan Vasist, Aman Malhotra, Tomer Shenhar, Pinal Tailor, Pinar Yilmaz, Michael Diamond, Michele Donini

CoRR, 2024

Explaining Probabilistic Models with Distributional Values.

Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography.

Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger

Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

Geographical Erasure in Language Generation.

Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Efficient fair PCA for fair representation learning.

Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Model AI Assignments 2023.

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

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Deep fair models for complex data: Graphs labeling and explainable face recognition.

Danilo Franco, Nicolò Navarin, Michele Donini, Davide Anguita, Luca Oneto

Neurocomputing, 2022

Diverse Counterfactual Explanations for Anomaly Detection in Time Series.

Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau

CoRR, 2022

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models.

David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

More Than Words: Towards Better Quality Interpretations of Text Classifiers.

Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi

CoRR, 2021

Multi-objective Asynchronous Successive Halving.

Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau

CoRR, 2021

Voting with random classifiers (VORACE): theoretical and experimental analysis.

Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi

Auton. Agents Multi Agent Syst., 2021

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization.

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

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud.

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

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Fair Bayesian Optimization.

Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

On the Lack of Robust Interpretability of Neural Text Classifiers.

Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi

Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020

Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy.

Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor

Neurocomputing, 2020

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization.

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

CoRR, 2020

Fair Bayesian Optimization.

Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau

CoRR, 2020

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning.

Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

General Fair Empirical Risk Minimization.

Luca Oneto, Michele Donini, Massimiliano Pontil

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Marthe: Scheduling the Learning Rate Via Online Hypergradients.

Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Learning Deep Fair Graph Neural Networks.

Luca Oneto, Nicolò Navarin, Michele Donini

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Learning Fair and Transferable Representations with Theoretical Guarantees.

Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

Voting with Random Classifiers (VORACE).

Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.

Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda

NeuroImage, 2019

Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm.

Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi

CoRR, 2019

Learning Fair and Transferable Representations.

Luca Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil

CoRR, 2019

PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors.

Luca Oneto, Michele Donini, Massimiliano Pontil

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Taking Advantage of Multitask Learning for Fair Classification.

Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels.

Luca Oneto, Nicolò Navarin, Michele Donini, Sandro Ridella, Alessandro Sperduti, Fabio Aiolli, Davide Anguita

IEEE Trans. Neural Networks Learn. Syst., 2018

Scuba: scalable kernel-based gene prioritization.

Guido Zampieri, Dinh Tran-Van, Michele Donini, Nicolò Navarin, Fabio Aiolli, Alessandro Sperduti, Giorgio Valle

BMC Bioinform., 2018

Empirical Risk Minimization Under Fairness Constraints.

Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Emerging trends in machine learning: beyond conventional methods and data.

Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Voting with Random Neural Networks: a Democratic Ensemble Classifier.

Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi

Proceedings of the RiCeRcA Workshop co-located with the 17th International Conference of the Italian Association for Artificial Intelligence, 2018

2017

Learning deep kernels in the space of dot product polynomials.

Michele Donini, Fabio Aiolli

Mach. Learn., 2017

Measuring the expressivity of graph kernels through Statistical Learning Theory.

Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita

Neurocomputing, 2017

A Bridge Between Hyperparameter Optimization and Larning-to-learn.

Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil

CoRR, 2017

A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion.

Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil

Proceedings of the 18th Annual Conference of the International Speech Communication Association, 2017

Forward and Reverse Gradient-Based Hyperparameter Optimization.

Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil

Proceedings of the 34th International Conference on Machine Learning, 2017

On Hyperparameter Optimization in Learning Systems.

Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil

Proceedings of the 5th International Conference on Learning Representations, 2017

Learning dot-product polynomials for multiclass problems.

Ivano Lauriola, Michele Donini, Fabio Aiolli

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning.

Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

Stairstep recognition and counting in a serious Game for increasing users' physical activity.

Matteo Ciman, Michele Donini, Ombretta Gaggi, Fabio Aiolli

Pers. Ubiquitous Comput., 2016

A multimodal multiple kernel learning approach to Alzheimer's disease detection.

Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

Distributed variance regularized Multitask Learning.

Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity.

Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints.

Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2015

EasyMKL: a scalable multiple kernel learning algorithm.

Fabio Aiolli, Michele Donini

Neurocomputing, 2015

Multiple Graph-Kernel Learning.

Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

Feature and kernel learning.

Verónica Bolón-Canedo, Michele Donini, Fabio Aiolli

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014

ClimbTheWorld: real-time stairstep counting to increase physical activity.

Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi

Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, 2014

Learning Anisotropic RBF Kernels.

Fabio Aiolli, Michele Donini

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

Easy multiple kernel learning.

Fabio Aiolli, Michele Donini

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014