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2020 – today
- 2024
[i7]Vitalii Emelianov, Michaël Perrot:
On the Impact of Output Perturbation on Fairness in Binary Linear Classification. CoRR abs/2402.03011 (2024)- 2023
- [j2]Gaurav Maheshwari, Michaël Perrot:
FairGrad: Fairness Aware Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023) - [j1]Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar:
A Revenue Function for Comparison-Based Hierarchical Clustering. Trans. Mach. Learn. Res. 2023 (2023) - [c11]Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi:
Differential Privacy has Bounded Impact on Fairness in Classification. ICML 2023: 23681-23705 - 2022
- [c10]Tanguy Kerdoncuff, Rémi Emonet, Michaël Perrot, Marc Sebban:
Optimal Tensor Transport. AAAI 2022: 7124-7132 - [i6]Gaurav Maheshwari, Michaël Perrot:
FairGrad: Fairness Aware Gradient Descent. CoRR abs/2206.10923 (2022) - [i5]Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi:
Fairness Certificates for Differentially Private Classification. CoRR abs/2210.16242 (2022) - [i4]Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar
:
A Revenue Function for Comparison-Based Hierarchical Clustering. CoRR abs/2211.16459 (2022) - 2020
- [c9]Michael Lohaus, Michaël Perrot, Ulrike von Luxburg:
Too Relaxed to Be Fair. ICML 2020: 6360-6369 - [c8]Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Near-Optimal Comparison Based Clustering. NeurIPS 2020 - [i3]Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Near-Optimal Comparison Based Clustering. CoRR abs/2010.03918 (2020)
2010 – 2019
- 2019
- [c7]Michaël Perrot, Ulrike von Luxburg:
Boosting for Comparison-Based Learning. IJCAI 2019: 1844-1850 - [c6]Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. NeurIPS 2019: 7454-7464 - 2018
- [i2]Michaël Perrot, Ulrike von Luxburg:
Boosting for Comparison-Based Learning. CoRR abs/1810.13333 (2018) - [i1]Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. CoRR abs/1811.00928 (2018) - 2016
- [b1]Michaël Perrot:
Theory and algorithms for learning metrics with controlled behaviour. (Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé). University of Lyon, France, 2016 - [c5]Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard:
Mapping Estimation for Discrete Optimal Transport. NIPS 2016: 4197-4205 - 2015
- [c4]Michaël Perrot, Amaury Habrard:
A Theoretical Analysis of Metric Hypothesis Transfer Learning. ICML 2015: 1708-1717 - [c3]Michaël Perrot, Amaury Habrard:
Regressive Virtual Metric Learning. NIPS 2015: 1810-1818 - 2014
- [c2]Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban:
Modeling Perceptual Color Differences by Local Metric Learning. ECCV (5) 2014: 96-111 - 2012
- [c1]Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban:
Speeding Up Syntactic Learning Using Contextual Information. ICGI 2012: 49-53
Coauthor Index
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last updated on 2024-08-02 19:17 CEST by the dblp team
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