Michaël Perrot

According to our database, Michaël Perrot authored at least 17 papers between 2012 and 2024.

Collaborative distances:

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

2024

Recipient Profiling: Predicting Characteristics from Messages.

Martin Borquez, Mikaela Keller, Michaël Perrot, Damien Sileo

CoRR, 2024

On the Impact of Output Perturbation on Fairness in Binary Linear Classification.

Vitalii Emelianov, Michaël Perrot

CoRR, 2024

2023

A Revenue Function for Comparison-Based Hierarchical Clustering.

Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar

Trans. Mach. Learn. Res., 2023

FairGrad: Fairness Aware Gradient Descent.

Gaurav Maheshwari, Michaël Perrot

Trans. Mach. Learn. Res., 2023

Differential Privacy has Bounded Impact on Fairness in Classification.

Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi

Proceedings of the International Conference on Machine Learning, 2023

2022

Fairness Certificates for Differentially Private Classification.

Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi

CoRR, 2022

Optimal Tensor Transport.

Tanguy Kerdoncuff, Rémi Emonet, Michaël Perrot, Marc Sebban

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2020

Near-Optimal Comparison Based Clustering.

Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar

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

Too Relaxed to Be Fair.

Michael Lohaus, Michaël Perrot, Ulrike von Luxburg

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Foundations of Comparison-Based Hierarchical Clustering.

Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Boosting for Comparison-Based Learning.

Michaël Perrot, Ulrike von Luxburg

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2016

Theory and algorithms for learning metrics with controlled behaviour. (Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé).

Michaël Perrot

PhD thesis, 2016

Mapping Estimation for Discrete Optimal Transport.

Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Regressive Virtual Metric Learning.

Michaël Perrot, Amaury Habrard

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Theoretical Analysis of Metric Hypothesis Transfer Learning.

Michaël Perrot, Amaury Habrard

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Modeling Perceptual Color Differences by Local Metric Learning.

Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban

Proceedings of the Computer Vision - ECCV 2014, 2014

2012

Speeding Up Syntactic Learning Using Contextual Information.

Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban

Proceedings of the Eleventh International Conference on Grammatical Inference, 2012