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06/01/2023
The Risks of Recourse in Binary Classification
Algorithmic recourse provides explanations that help users overturn an u...
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03/15/2023
Understanding Post-hoc Explainers: The Case of Anchors
In many scenarios, the interpretability of machine learning models is a ...
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03/09/2023
On the Robustness of Text Vectorizers
A fundamental issue in natural language processing is the robustness of ...
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12/06/2022
Explainability as statistical inference
A wide variety of model explanation approaches have been proposed in rec...
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07/04/2022
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
Complex machine learning algorithms are used more and more often in crit...
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05/27/2022
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
Anchors [Ribeiro et al. (2018)] is a post-hoc, rule-based interpretabili...
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01/23/2022
How to scale hyperparameters for quickshift image segmentation
Quickshift is a popular algorithm for image segmentation, used as a prep...
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11/16/2021
SMACE: A New Method for the Interpretability of Composite Decision Systems
Interpretability is a pressing issue for decision systems. Many post hoc...
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07/22/2021
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Algorithms involving Gaussian processes or determinantal point processes...
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02/11/2021
What does LIME really see in images?
The performance of modern algorithms on certain computer vision tasks su...
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10/23/2020
An Analysis of LIME for Text Data
Text data are increasingly handled in an automated fashion by machine le...
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08/25/2020
Looking deeper into LIME
Interpretability of machine learning algorithm is a pressing need. Numer...
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01/10/2020
Explaining the Explainer: A First Theoretical Analysis of LIME
Machine learning is used more and more often for sensitive applications,...
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06/18/2018
Comparison-Based Random Forests
Assume we are given a set of items from a general metric space, but we n...
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05/21/2018