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Damien Garreau
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2020 – today
- 2024
- [c15]Hidde Fokkema, Damien Garreau, Tim van Erven:
The Risks of Recourse in Binary Classification. AISTATS 2024: 550-558 - [c14]Gabriele Ciravegna, Mateo Espinosa Zarlenga, Pietro Barbiero, Francesco Giannini, Zohreh Shams, Damien Garreau, Mateja Jamnik, Tania Cerquitelli:
Workshop on Human-Interpretable AI. KDD 2024: 6708-6709 - [i19]Gianluigi Lopardo, Frédéric Precioso, Damien Garreau:
Attention Meets Post-hoc Interpretability: A Mathematical Perspective. CoRR abs/2402.03485 (2024) - [i18]Magamed Taimeskhanov, Ronan Sicre, Damien Garreau:
CAM-Based Methods Can See through Walls. CoRR abs/2404.01964 (2024) - 2023
- [j4]Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau:
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. J. Mach. Learn. Res. 24: 71:1-71:57 (2023) - [c13]Gianluigi Lopardo, Frédéric Precioso, Damien Garreau:
A Sea of Words: An In-Depth Analysis of Anchors for Text Data. AISTATS 2023: 4848-4879 - [c12]Rémi Catellier, Samuel Vaiter, Damien Garreau:
On the Robustness of Text Vectorizers. ICML 2023: 3782-3814 - [c11]Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei:
Explainability as statistical inference. ICML 2023: 30584-30612 - [i17]Rémi Catellier, Samuel Vaiter, Damien Garreau:
On the Robustness of Text Vectorizers. CoRR abs/2303.07203 (2023) - [i16]Gianluigi Lopardo, Frédéric Precioso, Damien Garreau:
Understanding Post-hoc Explainers: The Case of Anchors. CoRR abs/2303.08806 (2023) - [i15]Hidde Fokkema, Damien Garreau, Tim van Erven:
The Risks of Recourse in Binary Classification. CoRR abs/2306.00497 (2023) - [i14]Gianluigi Lopardo, Frédéric Precioso, Damien Garreau:
Faithful and Robust Local Interpretability for Textual Predictions. CoRR abs/2311.01605 (2023) - [i13]Pierre-Alexandre Mattei, Damien Garreau:
Are ensembles getting better all the time? CoRR abs/2311.17885 (2023) - 2022
- [c10]Damien Garreau:
How to scale hyperparameters for quickshift image segmentation. AISTATS 2022: 5243-5275 - [c9]Gianluigi Lopardo, Damien Garreau:
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data. ICPR Workshops (3) 2022: 261-268 - [c8]Gianluigi Lopardo, Damien Garreau, Frédéric Precioso, Greger Ottosson:
SMACE: A New Method for the Interpretability of Composite Decision Systems. ECML/PKDD (1) 2022: 325-339 - [c7]Buntheng Ly, Sonny Finsterbach, Marta Nuñez Garcia, Pierre Jaïs, Damien Garreau, Hubert Cochet, Maxime Sermesant:
Interpretable Prediction of Post-Infarct Ventricular Arrhythmia Using Graph Convolutional Network. STACOM@MICCAI 2022: 157-167 - [i12]Damien Garreau:
How to scale hyperparameters for quickshift image segmentation. CoRR abs/2201.09286 (2022) - [i11]Gianluigi Lopardo, Damien Garreau, Frédéric Precioso:
A Sea of Words: An In-Depth Analysis of Anchors for Text Data. CoRR abs/2205.13789 (2022) - [i10]Gianluigi Lopardo, Damien Garreau:
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data. CoRR abs/2207.01420 (2022) - [i9]Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei:
Explainability as statistical inference. CoRR abs/2212.03131 (2022) - 2021
- [j3]Luca Rendsburg
, Damien Garreau
:
Comparison-based centrality measures. Int. J. Data Sci. Anal. 11(3): 243-259 (2021) - [c6]Dina Mardaoui, Damien Garreau:
An Analysis of LIME for Text Data. AISTATS 2021: 3493-3501 - [c5]Damien Garreau, Dina Mardaoui:
What does LIME really see in images? ICML 2021: 3620-3629 - [i8]Damien Garreau, Dina Mardaoui:
What does LIME really see in images? CoRR abs/2102.06307 (2021) - [i7]Gianluigi Lopardo, Damien Garreau, Frédéric Precioso, Greger Ottosson:
SMACE: A New Method for the Interpretability of Composite Decision Systems. CoRR abs/2111.08749 (2021) - 2020
- [j2]Nicolas Keriven
, Damien Garreau
, Iacopo Poli
:
NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection. IEEE Trans. Signal Process. 68: 3515-3528 (2020) - [c4]Damien Garreau, Ulrike von Luxburg:
Explaining the Explainer: A First Theoretical Analysis of LIME. AISTATS 2020: 1287-1296 - [i6]Damien Garreau, Ulrike von Luxburg:
Explaining the Explainer: A First Theoretical Analysis of LIME. CoRR abs/2001.03447 (2020) - [i5]Damien Garreau, Ulrike von Luxburg:
Looking deeper into LIME. CoRR abs/2008.11092 (2020) - [i4]Dina Mardaoui, Damien Garreau
:
An Analysis of LIME for Text Data. CoRR abs/2010.12487 (2020)
2010 – 2019
- 2018
- [j1]Felix Schoeller
, Mickaël Eskinazi, Damien Garreau:
Dynamics of the knowledge instinct: Effects of incoherence on the cognitive system. Cogn. Syst. Res. 47: 85-91 (2018) - [c3]Siavash Haghiri, Damien Garreau, Ulrike von Luxburg:
Comparison-Based Random Forests. ICML 2018: 1866-1875 - [c2]Cheng Tang, Damien Garreau, Ulrike von Luxburg:
When do random forests fail? NeurIPS 2018: 2987-2997 - [i3]Nicolas Keriven
, Damien Garreau, Iacopo Poli:
NEWMA: a new method for scalable model-free online change-point detection. CoRR abs/1805.08061 (2018) - [i2]Siavash Haghiri, Damien Garreau, Ulrike von Luxburg:
Comparison-Based Random Forests. CoRR abs/1806.06616 (2018) - 2014
- [c1]Rémi Lajugie, Damien Garreau, Francis R. Bach, Sylvain Arlot:
Metric Learning for Temporal Sequence Alignment. NIPS 2014: 1817-1825 - [i1]Damien Garreau
, Rémi Lajugie, Sylvain Arlot, Francis R. Bach:
Metric Learning for Temporal Sequence Alignment. CoRR abs/1409.3136 (2014)
Coauthor Index
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last updated on 2024-08-28 23:34 CEST by the dblp team
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