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Alexandre Termier

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4 papers

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AAAI Conference 2024 Conference Paper

Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection

Gwladys Kelodjou
Laurence Rozé
Véronique Masson
Luis Galárraga
Romaric Gaudel
Maurice Tchuente
Alexandre Termier

Machine learning techniques, such as deep learning and ensemble methods, are widely used in various domains due to their ability to handle complex real-world tasks. However, their black-box nature has raised multiple concerns about the fairness, trustworthiness, and transparency of computer-assisted decision-making. This has led to the emergence of local post-hoc explainability methods, which offer explanations for individual decisions made by black-box algorithms. Among these methods, Kernel SHAP is widely used due to its model-agnostic nature and its well-founded theoretical framework. Despite these strengths, Kernel SHAP suffers from high instability: different executions of the method with the same inputs can lead to significantly different explanations, which diminishes the relevance of the explanations. The contribution of this paper is two-fold. On the one hand, we show that Kernel SHAP's instability is caused by its stochastic neighbor selection procedure, which we adapt to achieve full stability without compromising explanation fidelity. On the other hand, we show that by restricting the neighbors generation to perturbations of size 1 -- which we call the coalitions of Layer 1 -- we obtain a novel feature-attribution method that is fully stable, computationally efficient, and still meaningful.

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AAAI Conference 2022 Conference Paper

TAG: Learning Timed Automata from Logs

Lénaïg Cornanguer
Christine Largouët
Laurence Rozé
Alexandre Termier

Event logs are often one of the main sources of information to understand the behavior of a system. While numerous approaches have extracted partial information from event logs, in this work, we aim at inferring a global model of a system from its event logs. We consider real-time systems, which can be modeled with Timed Automata: our approach is thus a Timed Automata learner. There is a handful of related work, however, they might require a lot of parameters or produce Timed Automata that either are undeterministic or lack precision. In contrast, our proposed approach, called TAG, requires only one parameter and learns a deterministic Timed Automaton having a good tradeoff between accuracy and complexity of the automata. This allows getting an interpretable and accurate global model of the real-time system considered. Our experiments compare our approach to the related work and demonstrate its merits.

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AAAI Conference 2020 Conference Paper

A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning

Kevin Fauvel
Daniel Balouek-Thomert
Diego Melgar
Pedro Silva
Anthony Simonet
Gabriel Antoniu
Alexandru Costan
Véronique Masson

Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a signiﬁcant volume of data consequently, affecting the response time and the robustness of EEW systems. In practice, EEW can be seen as a typical classiﬁcation problem in the machine learning ﬁeld: multi-sensor data are given in input, and earthquake severity is the classiﬁcation result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a realworld dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an ef- ﬁcient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometeronly approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.

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AAAI Conference 2015 Conference Paper

Improved Local Search for Binary Matrix Factorization

Seyed Hamid Mirisaee
Eric Gaussier
Alexandre Termier

Rank K Binary Matrix Factorization (BMF) approximates a binary matrix by the product of two binary matrices of lower rank, K, using either L1 or L2 norm. In this paper, we ﬁrst show that the BMF with L2 norm can be reformulated as an Unconstrained Binary Quadratic Programming (UBQP) problem. We then review several local search strategies that can be used to improve the BMF solutions obtained by previously proposed methods, before introducing a new local search dedicated to the BMF problem. We show in particular that the proposed solution is in general faster than the previously proposed ones. We then assess its behavior on several collections and methods and show that it signiﬁcantly improves methods targeting the L2 norms on all the datasets considered; for the L1 norm, the improvement is also signiﬁcant for real, structured datasets and for the BMF problem without the binary reconstruction constraint.

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