51 citations · 110 across the 17 of their papers we have counts for
19 papers
Attention Meets Post-hoc Interpretability: A Mathematical Perspective
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Attention-based architectures, in particular transformers, are at the heart of a technological revolution. Interestingly, in addition to helping obtain state-of-the-art results on…
Faithful and Robust Local Interpretability for Textual Predictions
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Interpretability is essential for machine learning models to be trusted and deployed in critical domains. However, existing methods for interpreting text models are often complex,…
The Risks of Recourse in Binary Classification
Hidde Fokkema, Damien Garreau, Tim van Erven
Algorithmic recourse provides explanations that help users overturn an unfavorable decision by a machine learning system. But so far very little attention has been paid to whether…
Understanding Post-hoc Explainers: The Case of Anchors
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
In many scenarios, the interpretability of machine learning models is a highly required but difficult task. To explain the individual predictions of such models, local model-agnost…
On the Robustness of Text Vectorizers
Rémi Catellier, Samuel Vaiter, Damien Garreau
A fundamental issue in machine learning is the robustness of the model with respect to changes in the input. In natural language processing, models typically contain a first embedd…
Explainability as statistical inference
Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen +1
A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and ca…
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
Gianluigi Lopardo, Damien Garreau
Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods,…
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Anchors (Ribeiro et al., 2018) is a post-hoc, rule-based interpretability method. For text data, it proposes to explain a decision by highlighting a small set of words (an anchor)…
How to scale hyperparameters for quickshift image segmentation
Damien Garreau
Quickshift is a popular algorithm for image segmentation, used as a preprocessing step in many applications. Unfortunately, it is quite challenging to understand the hyperparameter…
SMACE: A New Method for the Interpretability of Composite Decision Systems
Gianluigi Lopardo, Damien Garreau, Frederic Precioso +1
Interpretability is a pressing issue for decision systems. Many post hoc methods have been proposed to explain the predictions of a single machine learning model. However, business…
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels, Wouter Boomsma, Jes Frellsen +1
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the…
What does LIME really see in images?
Damien Garreau, Dina Mardaoui
The performance of modern algorithms on certain computer vision tasks such as object recognition is now close to that of humans. This success was achieved at the price of complicat…
An Analysis of LIME for Text Data
Dina Mardaoui, Damien Garreau
Text data are increasingly handled in an automated fashion by machine learning algorithms. But the models handling these data are not always well-understood due to their complexity…
Looking Deeper into Tabular LIME
Damien Garreau, Ulrike von Luxburg
In this paper, we present a thorough theoretical analysis of the default implementation of LIME in the case of tabular data. We prove that in the large sample limit, the interpreta…
Explaining the Explainer: A First Theoretical Analysis of LIME
Damien Garreau, Ulrike von Luxburg
Machine learning is used more and more often for sensitive applications, sometimes replacing humans in critical decision-making processes. As such, interpretability of these algori…
Comparison-Based Random Forests
Siavash Haghiri, Damien Garreau, Ulrike von Luxburg
Assume we are given a set of items from a general metric space, but we neither have access to the representation of the data nor to the distances between data points. Instead, supp…
NEWMA: a new method for scalable model-free online change-point detection
Nicolas Keriven, Damien Garreau, Iacopo Poli
We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new…
Consistent change-point detection with kernels
Damien Garreau, Sylvain Arlot
In this paper we study the kernel change-point algorithm (KCP) proposed by Arlot, Celisse and Harchaoui (2012), which aims at locating an unknown number of change-points in the dis…
Metric Learning for Temporal Sequence Alignment
Damien Garreau, Rémi Lajugie, Sylvain Arlot +1
In this paper, we propose to learn a Mahalanobis distance to perform alignment of multivariate time series. The learning examples for this task are time series for which the true a…