Easy multiple kernel learning. - Fabio Aiolli; Michele Donini (ESANN, 2014)

Learning Anisotropic RBF Kernels. - Fabio Aiolli; Michele Donini (ICANN, 2014)

ClimbTheWorld: real-time stairstep counting to increase physical activity. - Fabio Aiolli; Matteo Ciman; Michele Donini; Ombretta Gaggi (MobiQuitous, 2014)

Feature and kernel learning. - Verónica Bolón-Canedo; Michele Donini; Fabio Aiolli (ESANN, 2015)

Multiple Graph-Kernel Learning. - Fabio Aiolli; Michele Donini; Nicolò Navarin; Alessandro Sperduti (SSCI, 2015)

EasyMKL: a scalable multiple kernel learning algorithm. - Fabio Aiolli; Michele Donini (Neurocomputing, 2015)

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. - Luca Oneto; Nicolò Navarin; Michele Donini; Fabio Aiolli; Davide Anguita (ESANN, 2016)

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. - Luca Oneto; Nicolò Navarin; Michele Donini; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (ESANN, 2016)

Distributed variance regularized Multitask Learning. - Michele Donini; David Martínez-Rego; Martin Goodson; John Shawe-Taylor; Massimiliano Pontil (IJCNN, 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 (MLSP, 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)

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. - Michele Donini; Nicolò Navarin; Ivano Lauriola; Fabio Aiolli; Fabrizio Costa (ESANN, 2017)

Learning dot-product polynomials for multiclass problems. - Ivano Lauriola; Michele Donini; Fabio Aiolli (ESANN, 2017)

On Hyperparameter Optimization in Learning Systems. - Luca Franceschi 001; Michele Donini; Paolo Frasconi; Massimiliano Pontil (ICLR (Workshop), 2017)

Forward and Reverse Gradient-Based Hyperparameter Optimization. - Luca Franceschi 001; Michele Donini; Paolo Frasconi; Massimiliano Pontil (ICML, 2017)

A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. - Leonardo Badino; Luca Franceschi 001; Raman Arora; Michele Donini; Massimiliano Pontil (INTERSPEECH, 2017)

A Bridge Between Hyperparameter Optimization and Larning-to-learn. - Luca Franceschi 001; Michele Donini; Paolo Frasconi; Massimiliano Pontil (CoRR, 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)

Learning deep kernels in the space of dot product polynomials. - Michele Donini; Fabio Aiolli (Mach. Learn., 2017)

Voting with Random Neural Networks: a Democratic Ensemble Classifier. - Michele Donini; Andrea Loreggia; Maria Silvia Pini; Francesca Rossi 001 (RiCeRcA@AI*IA, 2018)

Emerging trends in machine learning: beyond conventional methods and data. - Luca Oneto; Nicolò Navarin; Michele Donini; Davide Anguita (ESANN, 2018)

Empirical Risk Minimization Under Fairness Constraints. - Michele Donini; Luca Oneto; Shai Ben-David; John Shawe-Taylor; Massimiliano Pontil (NeurIPS, 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)

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)

Taking Advantage of Multitask Learning for Fair Classification. - Luca Oneto; Michele Donini; Amon Elders; Massimiliano Pontil (AIES, 2019)

PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. - Luca Oneto; Michele Donini; Massimiliano Pontil (ESANN, 2019)

Learning Fair and Transferable Representations. - Luca Oneto; Michele Donini; Andreas Maurer; Massimiliano Pontil (CoRR, 2019)

Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. - Michele Donini; Luca Franceschi 001; Massimiliano Pontil; Orchid Majumder; Paolo Frasconi (CoRR, 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)

Voting with Random Classifiers (VORACE). - Cristina Cornelio; Michele Donini; Andrea Loreggia; Maria Silvia Pini; Francesca Rossi 001 (AAMAS, 2020)

Learning Fair and Transferable Representations with Theoretical Guarantees. - Luca Oneto; Michele Donini; Massimiliano Pontil; Andreas Maurer (DSAA, 2020)

Learning Deep Fair Graph Neural Networks. - Luca Oneto; Nicolò Navarin; Michele Donini (ESANN, 2020)

Marthe: Scheduling the Learning Rate Via Online Hypergradients. - Michele Donini; Luca Franceschi 001; Orchid Majumder; Massimiliano Pontil; Paolo Frasconi (IJCAI, 2020)

General Fair Empirical Risk Minimization. - Luca Oneto; Michele Donini; Massimiliano Pontil (IJCNN, 2020)

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. - Luca Oneto; Michele Donini; Giulia Luise; Carlo Ciliberto; Andreas Maurer; Massimiliano Pontil (NeurIPS, 2020)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2020)

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (CoRR, 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)

On the Lack of Robust Interpretability of Neural Text Classifiers. - Muhammad Bilal Zafar; Michele Donini; Dylan Slack; Cédric Archambeau; Sanjiv Das; Krishnaram Kenthapadi (ACL/IJCNLP (Findings), 2021)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Muhammad Bilal Zafar; Robin Schmucker; Krishnaram Kenthapadi; Cédric Archambeau (AIES, 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; et al. (KDD, 2021)

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (KDD, 2021)

Multi-objective Asynchronous Successive Halving. - Robin Schmucker; Michele Donini; Muhammad Bilal Zafar; David Salinas; Cédric Archambeau (CoRR, 2021)

More Than Words: Towards Better Quality Interpretations of Text Classifiers. - Muhammad Bilal Zafar; Philipp Schmidt 002; Michele Donini; Cédric Archambeau; Felix Biessmann; Sanjiv Ranjan Das; Krishnaram Kenthapadi (CoRR, 2021)

Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis. - Cristina Cornelio; Michele Donini; Andrea Loreggia; Maria Silvia Pini; Francesca Rossi 001 (AAMAS, 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 (KDD, 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)

Deep fair models for complex data: Graphs labeling and explainable face recognition. - Danilo Franco; Nicolò Navarin; Michele Donini; Davide Anguita; Luca Oneto (Neurocomputing, 2022)

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; et al. (AAAI, 2023)

Efficient fair PCA for fair representation learning. - Matthäus Kleindessner; Michele Donini; Chris Russell 001; Muhammad Bilal Zafar (AISTATS, 2023)

Geographical Erasure in Language Generation. - Pola Schwöbel; Jacek Golebiowski; Michele Donini; Cédric Archambeau; Danish Pruthi (EMNLP (Findings), 2023)

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. - Luca Franceschi 001; Cemre Zor; Muhammad Bilal Zafar; Gianluca Detommaso; Cédric Archambeau; Tamas Madl; Michele Donini; Matthias W. Seeger (TML4H, 2023)

Explaining Probabilistic Models with Distributional Values. - Luca Franceschi 001; Michele Donini; Cédric Archambeau; Matthias W. Seeger (ICML, 2024)

Evaluating Large Language Models with fmeval. - Pola Schwöbel; Luca Franceschi 001; Muhammad Bilal Zafar; Keerthan Vasist; Aman Malhotra; Tomer Shenhar; Pinal Tailor; Pinar Yilmaz; Michael Diamond; Michele Donini (CoRR, 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 001; Michele Donini; Valerio Perrone; Aaron Klein; Cédric Archambeau; Matthias W. Seeger; Massimiliano Pontil; Paolo Frasconi (Found. Trends Mach. Learn., 2025)