Michele Donini
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
28
Venues
15
Active years
2014–2024
Best venue rank
A*
Where they publish
Papers
28 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | ICML | Explaining Probabilistic Models with Distributional Values. | Luca Franceschi, Michele Donini, Cdric Archambeau, Matthias W. Seeger |
| 2023 | AAAI | Model AI Assignments 2023. | Todd W. Neller, Raechel Walker, Olivia Dias, Zeynep Yalin, Cynthia Breazeal, Matthew E. Taylor, Michele Donini, Erin J. Talvitie, Charlie Pilgrim, Paolo Turrini, James Maher, Matthew Boutell, Justin Wilson, Narges Norouzi, Jonathan Scott |
| 2023 | AISTATS | Efficient fair PCA for fair representation learning. | Matthus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar |
| 2023 | EMNLP | Geographical Erasure in Language Generation. | Pola Schwbel, Jacek Golebiowski, Michele Donini, Cdric Archambeau, Danish Pruthi |
| 2022 | KDD | Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. | David Nigenda, Zohar S. Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi |
| 2021 | ACL | On the Lack of Robust Interpretability of Neural Text Classifiers. | Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cdric Archambeau, Sanjiv Das, Krishnaram Kenthapadi |
| 2021 | AIES | Fair Bayesian Optimization. | Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cdric Archambeau |
| 2021 | KDD | 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 |
| 2021 | KDD | 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, Cdric Archambeau |
| 2020 | DSAA | Learning Fair and Transferable Representations with Theoretical Guarantees. | Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer |
| 2020 | ESANN | Learning Deep Fair Graph Neural Networks. | Luca Oneto, Nicol Navarin, Michele Donini |
| 2020 | IJCAI | Marthe: Scheduling the Learning Rate Via Online Hypergradients. | Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi |
| 2020 | IJCNN | General Fair Empirical Risk Minimization. | Luca Oneto, Michele Donini, Massimiliano Pontil |
| 2019 | AIES | Taking Advantage of Multitask Learning for Fair Classification. | Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil |
| 2019 | ESANN | PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. | Luca Oneto, Michele Donini, Massimiliano Pontil |
| 2018 | ESANN | Emerging trends in machine learning: beyond conventional methods and data. | Luca Oneto, Nicol Navarin, Michele Donini, Davide Anguita |
| 2017 | ESANN | Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. | Michele Donini, Nicol Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa |
| 2017 | ESANN | Learning dot-product polynomials for multiclass problems. | Ivano Lauriola, Michele Donini, Fabio Aiolli |
| 2017 | ICLR | On Hyperparameter Optimization in Learning Systems. | Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil |
| 2017 | ICML | Forward and Reverse Gradient-Based Hyperparameter Optimization. | Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil |
| 2017 | Interspeech | A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. | Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil |
| 2016 | ESANN | Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. | Luca Oneto, Nicol Navarin, Michele Donini, Fabio Aiolli, Davide Anguita |
| 2016 | ESANN | Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. | Luca Oneto, Nicol Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita |
| 2016 | IJCNN | Distributed variance regularized Multitask Learning. | Michele Donini, David Martnez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil |
| 2015 | ESANN | Feature and kernel learning. | Vernica Boln-Canedo, Michele Donini, Fabio Aiolli |
| 2014 | ESANN | Easy multiple kernel learning. | Fabio Aiolli, Michele Donini |
| 2014 | ICANN | Learning Anisotropic RBF Kernels. | Fabio Aiolli, Michele Donini |
| 2014 | Mobiquitous | ClimbTheWorld: real-time stairstep counting to increase physical activity. | Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi |