Céline Robardet

In Proceedings of the 31st international joint conference on artificial intelligence (IJCAI’22)

Abstract

GNNs are efficient for classifying graphs but their internal workings is opaque which limits their field of application. Existing methods for explaining GNN focus on disclosing the relationships between input graphs and the model’s decision. In contrary, the method we propose isolates internal features, hidden in the network layers, which are automatically identified by the GNN to classify graphs. We show that this method makes it possible to know the parts of the input graphs used by GNN with much less bias than the SOTA methods and therefore to provide confidence in the decision process.

Qu’est-ce que mon GNN capture vraiment ? Exploration des représentations internes d’un GNN

By Luca Veyrin-Forrer, Ataollah Kamal, Stefan Duffner, Marc Plantevit, Céline Robardet

2022-03-24

In Extraction et gestion des connaissances, EGC 2022, blois, france, 24 au 28 janvier 2022

Abstract

While existing GNN’s explanation methods explain the decision by studying the output layer, we propose a method that analyzes the hidden layers to identify the neurons that are co-activated for a class. We associate to them a graph.

Electricity price forecasting on the day-ahead market using machine learning

By Léonard Tschora, Erwan Pierre, Marc Plantevit, Céline Robardet

2022-03-10

In Applied Energy

Abstract

The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own constraints (volume of production, dependence on the weather, or production inertia), and by the difficulty of its storage. Being able to predict the prices of the next day is an important issue, to allow the development of intelligent uses of electricity. In this article, we investigate the capabilities of different machine learning techniques to accurately predict electricity prices. Specifically, we extend current state-of-the-art approaches by considering previously unused predictive features such as price histories of neighboring countries. We show that these features significantly improve the quality of forecasts, even in the current period when sudden changes are occurring. We also develop an analysis of the contribution of the different features in model prediction using Shap values, in order to shed light on how models make their prediction and to build user confidence in models.

Découverte de sous-groupes de prédictions interprétables pour le triage d’incidents

By Youcef Remil, Anes Bendimerad, Marc Plantevit, Céline Robardet, Mehdi Kaytoue

2022-01-24

In Extraction et gestion des connaissances, EGC 2022, blois, france, 24 au 28 janvier 2022

Abstract

The need for predictive maintenance comes with an increasing number of incidents, where it is imperative to quickly decide which service to contact for corrective actions. Several predictive models have been designed to automate this process, but the efficient models are opaque (say, black boxes). Many approaches have been proposed to locally explain each prediction of such models. However, providing an explanation for every result is not conceivable when it comes to a large number of daily predictions to analyze. In this article we propose a method based on Subgroup Discovery in order to (1) group together objects that share similar explanations and (2) provide a description that characterises each subgroup

What does my GNN really capture? On exploring internal GNN representations

Abstract

Qu’est-ce que mon GNN capture vraiment ? Exploration des représentations internes d’un GNN

Abstract

Electricity price forecasting on the day-ahead market using machine learning

Abstract

Découverte de sous-groupes de prédictions interprétables pour le triage d’incidents

Abstract

Search

Tags