Michele Donini

69 papers A* 9A 4B 12C 4Journal 33Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Hyperparameter Optimization in Machine Learning. Found. Trends Mach. Learn. Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2024	J	`jnl`	Evaluating Large Language Models with fmeval. CoRR Pola Schwöbel, Luca Franceschi, Muhammad Bilal Zafar, Keerthan Vasist, Aman Malhotra, Tomer Shenhar, Pinal Tailor, Pinar Yilmaz, Michael Diamond, Michele Donini
2024	A*	`conf`	Explaining Probabilistic Models with Distributional Values. ICML Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024	J	`jnl`	Explaining Probabilistic Models with Distributional Values. CoRR Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2024	J	`jnl`	Hyperparameter Optimization in Machine Learning. CoRR Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2023	A	`conf`	Efficient fair PCA for fair representation learning. AISTATS Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
2023	J	`jnl`	Efficient fair PCA for fair representation learning. CoRR Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar
2023	—	`conf`	Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger
2023	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2023	—	`conf`	Geographical Erasure in Language Generation. EMNLP (Findings) Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
2023	J	`jnl`	Geographical Erasure in Language Generation. CoRR Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau, Danish Pruthi
2023	A*	`conf`	Model AI Assignments 2023. AAAI 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
2022	A*	`conf`	Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. KDD David Nigenda, Zohar S. Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi
2022	J	`jnl`	Deep fair models for complex data: Graphs labeling and explainable face recognition. Neurocomputing Danilo Franco, Nicolò Navarin, Michele Donini, Davide Anguita, Luca Oneto
2022	J	`jnl`	Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau
2022	A	`conf`	Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis. AAMAS Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
2021	A*	`conf`	Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD 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
2021	A*	`conf`	Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. KDD 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
2021	J	`jnl`	Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. CoRR 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
2021	J	`jnl`	Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. CoRR David Nigenda, Zohar S. Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi
2021	C	`conf`	Fair Bayesian Optimization. AIES Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
2021	J	`jnl`	More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi
2021	J	`jnl`	Multi-objective Asynchronous Successive Halving. CoRR Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
2021	—	`conf`	On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
2021	J	`jnl`	On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
2021	J	`jnl`	Voting with random classifiers (VORACE): theoretical and experimental analysis. Auton. Agents Multi Agent Syst. Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
2020	J	`jnl`	Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR 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
2020	A*	`conf`	Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. NeurIPS Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil
2020	J	`jnl`	Fair Bayesian Optimization. CoRR Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cédric Archambeau
2020	B	`conf`	General Fair Empirical Risk Minimization. IJCNN Luca Oneto, Michele Donini, Massimiliano Pontil
2020	B	`conf`	Learning Deep Fair Graph Neural Networks. ESANN Luca Oneto, Nicolò Navarin, Michele Donini
2020	B	`conf`	Learning Fair and Transferable Representations with Theoretical Guarantees. DSAA Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer
2020	A*	`conf`	Marthe: Scheduling the Learning Rate Via Online Hypergradients. IJCAI Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi
2020	J	`jnl`	Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor
2020	A	`conf`	Voting with Random Classifiers (VORACE). AAMAS Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
2019	J	`jnl`	Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda
2019	J	`jnl`	General Fair Empirical Risk Minimization. CoRR Luca Oneto, Michele Donini, Massimiliano Pontil
2019	J	`jnl`	Learning Fair and Transferable Representations. CoRR Luca Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil
2019	B	`conf`	PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. ESANN Luca Oneto, Michele Donini, Massimiliano Pontil
2019	J	`jnl`	Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. CoRR Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi
2019	C	`conf`	Taking Advantage of Multitask Learning for Fair Classification. AIES Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
2019	J	`jnl`	Voting with Random Classifiers (VORACE). CoRR Cristina Cornelio, Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
2018	B	`conf`	Emerging trends in machine learning: beyond conventional methods and data. ESANN Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita
2018	A*	`conf`	Empirical Risk Minimization Under Fairness Constraints. NeurIPS Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
2018	J	`jnl`	Empirical Risk Minimization under Fairness Constraints. CoRR Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
2018	J	`jnl`	Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Trans. Neural Networks Learn. Syst. Luca Oneto, Nicolò Navarin, Michele Donini, Sandro Ridella, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
2018	J	`jnl`	Scuba: scalable kernel-based gene prioritization. BMC Bioinform. Guido Zampieri, Dinh Tran-Van, Michele Donini, Nicolò Navarin, Fabio Aiolli, Alessandro Sperduti, Giorgio Valle
2018	J	`jnl`	Taking Advantage of Multitask Learning for Fair Classification. CoRR Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
2018	—	`conf`	Voting with Random Neural Networks: a Democratic Ensemble Classifier. RiCeRcA@AI*IA Michele Donini, Andrea Loreggia, Maria Silvia Pini, Francesca Rossi
2017	J	`jnl`	A Bridge Between Hyperparameter Optimization and Larning-to-learn. CoRR Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
2017	A	`conf`	A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. INTERSPEECH Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil
2017	B	`conf`	Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa
2017	A*	`conf`	Forward and Reverse Gradient-Based Hyperparameter Optimization. ICML Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
2017	J	`jnl`	Learning deep kernels in the space of dot product polynomials. Mach. Learn. Michele Donini, Fabio Aiolli
2017	B	`conf`	Learning dot-product polynomials for multiclass problems. ESANN Ivano Lauriola, Michele Donini, Fabio Aiolli
2017	J	`jnl`	Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
2017	—	`conf`	On Hyperparameter Optimization in Learning Systems. ICLR (Workshop) Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
2016	—	`conf`	A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda
2016	B	`conf`	Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. ESANN Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita
2016	B	`conf`	Distributed variance regularized Multitask Learning. IJCNN Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil
2016	B	`conf`	Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita
2016	J	`jnl`	Stairstep recognition and counting in a serious Game for increasing users' physical activity. Pers. Ubiquitous Comput. Matteo Ciman, Michele Donini, Ombretta Gaggi, Fabio Aiolli
2015	J	`jnl`	EasyMKL: a scalable multiple kernel learning algorithm. Neurocomputing Fabio Aiolli, Michele Donini
2015	B	`conf`	Feature and kernel learning. ESANN Verónica Bolón-Canedo, Michele Donini, Fabio Aiolli
2015	—	`conf`	Multiple Graph-Kernel Learning. SSCI Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti
2014	C	`conf`	ClimbTheWorld: real-time stairstep counting to increase physical activity. MobiQuitous Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi
2014	B	`conf`	Easy multiple kernel learning. ESANN Fabio Aiolli, Michele Donini
2014	C	`conf`	Learning Anisotropic RBF Kernels. ICANN Fabio Aiolli, Michele Donini