Céline Robardet

A simple yet effective interpretable bayesian personalized ranking for cognitive diagnosis

By Arthur Batel, Idir Benouaret, Joan Fruitet, Marc Plantevit, Céline Robardet

2024-10-10

In ECAI 2024 - 27th european conference on artificial intelligence

Abstract

In the field of education, the automatic assessment of student profiles has become a crucial objective, driven by the rapid expansion of online tutoring systems and computerized adaptive testing. These technologies aim to democratize education and enhance student assessment by providing detailed insights into student profiles, which are essential for accurately predicting the outcomes of exercises, such as solving various types of mathematical equations. We aim to develop a model capable of predicting responses to a large set of questions within the Multi-Target Prediction framework while ensuring that this model is explainable, allowing us to quantify student performance in specific knowledge areas. Existing cognitive diagnosis algorithms often struggle to meet the dual requirement of accurately predicting exercise outcomes and maintaining interpretability. To address this challenge, we propose an alternative to the complexity of current advanced machine learning models. Instead, we introduce a direct yet highly effective Bayesian Personalized Ranking algorithm, called CD-BPR, which incorporates interpretability as a core learning objective. Extensive experiments demonstrate that CD-BPR not only performs better in predicting exercise outcomes but also provides superior interpretability of estimated student profiles, thus fulfilling both key requirements.

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Transparent explainable logic layers

By Alessio Ragno, Marc Plantevit, Céline Robardet, Roberto Capobianco

2024-10-10

In ECAI 2024 - 27th european conference on artificial intelligence

Abstract

Explainable AI seeks to unveil the intricacies of black box models through post-hoc strategies or self-interpretable models. In this paper, we tackle the problem of building layers that are intrinsically explainable through logic rules. In particular, we address current state-of-the-art methods’ lack of fidelity and expressivity by introducing a transparent explainable logic layer (TELL). We propose to constrain a feed-forward layer with positive weights, which, combined with particular activation functions, offer the possibility of a direct translation into logic rules. Additionally, this approach overcomes the limitations of previous models, linked to their applicability to binary data only, by proposing a new way to automatically threshold real values and incorporate the obtained predicates into logic rules. We show that, compared to state-of-the-art, TELL achieves similar classification performances and, at the same time, provides higher explanatory power, measured by the agreement between models’ outputs and the activation of the logic explanations. In addition, TELL offers a broader spectrum of applications thanks to the possibility of its use on real data.

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DiffVersify: A scalable approach to differentiable pattern mining with coverage regularization

By Thibaut Chataing, Julien Perez, Marc Plantevit, Céline Robardet

2024-01-10

In Machine learning and knowledge discovery in databases. Research track - european conference, ECML PKDD 2024, vilnius, lithuania, september 9-13, 2024, proceedings, part VI

Abstract

Pattern mining addresses the challenge of automatically identifying interpretable and discriminative patterns within data. Recent approaches, leveraging differentiable approach through neural autoencoder with class recovery, have achieved encouraging results but tend to fall short as the magnitude of the noise and the number of underlying features increase in the data. Empirically, one can observe that the number of discovered patterns tend to be limited in these challenging contexts. In this article, we present a differentiable binary model that integrates a new regularization technique to enhance pattern coverage. Besides, we introduce an innovative pattern decoding strategy taking advantage of non-negative matrix factorization (NMF), extending beyond conventional thresholding methods prevalent in existing approaches. Experiments on four realworld datasets exhibit superior performances of DiffVersify in terms of the ROC-AUC metric. On synthetic data, we observe an increase in the similarity between the discovered patterns and the ground truth. Finally, using several metrics to finely evaluate the quality of the patterns in regard to the data, we show the global effectiveness of the approach.

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Forecasting electricity prices: An optimize then predict-based approach

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

2023-04-10

In Proceedings of the 21st international symposium on intelligent data analysis (IDA’23)

Abstract

We are interested in electricity price forecasting at the European scale. The electricity market is ruled by price regulation mechanisms that make it possible to adjust production to demand, as electricity is difficult to store. These mechanisms ensure the highest price for producers, the lowest price for consumers and a zero energy balance by setting day-ahead prices, i.e. prices for the next 24h. Most studies have focused on learning increasingly sophisticated models to predict the next day’s 24 hourly prices for a given zone. However, the zones are interdependent and this last point has hitherto been largely underestimated. In the following, we show that estimating the energy cross-border transfer by solving an optimization problem and integrating it as input of a model improves the performance of the price forecasting for several zones together.

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Electricity price forecasting based on order books: A differentiable optimization approach

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

2023-01-10

In Proceedings of the 10th IEEE international conference on data science and advanced analytics, (DSAA’23)

Abstract

We consider day-ahead electricity price forecasting on the European market. In this market, participants can offer electricity for sale or purchase for a specific price by submitting overnight orders. Market operators determine the market clearing price – the price at which the amount of electricity supplied equals the amount of electricity demanded – using the Euphemia balancing algorithm. euphemia is a quadratic optimization problem that maximizes the social welfare defined as the sum of the supplier surplus and consumer surplus while ensuring a null energy balance. This mechanism deeply influences the price calculation, but has so far been little considered in electricity price forecasting algorithms. Existing models are generally based on identifying relationships between exogenous characteristics (consumption and production forecasts) and the market clearing price to be predicted. A few studies have examined the euphemia mechanism during prediction, by doing costly manual transformations on order books. In this article, we overcome this limitation by considering the pricing mechanism during model training. For this, we use a predict-and-optimize strategy with differentiable optimization. We design a fully differentiable and scalable solving method for the euphemia optimization problem and apply it on real-life data from the European Power Exchange (EPEX). We design different model architectures using our differentiable solver and empirically study the impact of taking into account the optimal calculation of prices within the training of the neural network.

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Methods for explaining top-N recommendations through subgroup discovery

By Mouloud Iferroudjene, Corentin Lonjarret, Céline Robardet, Marc Plantevit, Martin Atzmueller

2022-12-10

In Data Mining and Knowledge Discovery

Abstract

Explainable Artificial Intelligence (XAI) has received a lot of attention over the past decade, with the proposal of many methods explaining black box classifiers such as neural networks. Despite the ubiquity of recommender systems in the digital world, only few researchers have attempted to explain their functioning, whereas one major obstacle to their use is the problem of societal acceptability and trustworthiness. Indeed, recommender systems direct user choices to a large extent and their impact is important as they give access to only a small part of the range of items (e.g., products and/or services), as the submerged part of the iceberg. Consequently, they limit access to other resources. The potentially negative effects of these systems have been pointed out as phenomena like echo chambers and winner-take-all effects, because the internal logic of these systems is to likely enclose the consumer in a deja vu loop. Therefore, it is crucial to provide explanations of such recommender systems and to identify the user data that led the respective system to make the individual recommendations. This then makes it possible to evaluate recommender systems not only regarding their effectiveness (i.e., their capability to recommend an item that was actually chosen by the user), but also with respect to the diversity, relevance and timeliness of the active data used for the recommendation. In this paper, we propose a deep analysis of two state-of-the-art models learnt on four datasets based on the identification of the items or the sequences of items actively used by the models. Our proposed methods are based on subgroup discovery with different pattern languages (i.e., itemsets and sequences). Specifically, we provide interpretable explanations of the recommendations of the Top-N items, which are useful to compare different models. Ultimately, these can then be used to present simple and understandable patterns to explain the reasons behind a generated recommendation to the user.

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In pursuit of the hidden features of GNN’s internal representations

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

2022-10-26

In Data & Knowledge Engineering

Abstract

We consider the problem of explaining Graph Neural Networks (GNNs). While most attempts aim at explaining the final decision of the model, we focus on the hidden layers to examine what the GNN actually captures and shed light on the hidden features built by the GNN. To that end, we first extract activation rules that identify sets of exceptionally co-activated neurons when classifying graphs in the same category. These rules define internal representations having a strong impact in the classification process. Then - this is the goal of the current paper - we interpret these rules by identifying a graph that is fully embedded in the related subspace identified by the rule. The graph search is based on a Monte Carlo Tree Search directed by a proximity measure between the graph embedding and the internal representation of the rule, as well as a realism factor that constrains the distribution of the labels of the graph to be similar to that observed on the dataset. Experiments including 6 real-world datasets and 3 baselines demonstrate that our method DISCERN generates realistic graphs of high quality which allows providing new insights into the respective GNN models.

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On GNN explainability with activation rules

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

2022-10-20

In Data Mining and Knowledge Discovery

Abstract

GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of societal acceptability and trustworthiness, properties which require making explicit the internal functioning of such models. Here, we propose to mine activation rules in the hidden layers to understand how the GNNs perceive the world. The problem is not to discover activation rules that are individually highly discriminating for an output of the model. Instead, the challenge is to provide a small set of rules that cover all input graphs. To this end, we introduce the subjective activation pattern domain. We define an effective and principled algorithm to enumerate activations rules in each hidden layer. The proposed approach for quantifying the interest of these rules is rooted in information theory and is able to account for background knowledge on the input graph data. The activation rules can then be redescribed thanks to pattern languages involving interpretable features. We show that the activation rules provide insights on the characteristics used by the GNN to classify the graphs. Especially, this allows to identify the hidden features built by the GNN through its different layers. Also, these rules can subsequently be used for explaining GNN decisions. Experiments on both synthetic and real-life datasets show highly competitive performance, with up to 200% improvement in fidelity on explaining graph classification over the SOTA methods.

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Discovering and visualizing tactics in table tennis games based on subgroup discovery

By Pierre Duluard, Xinqing Li, Marc Plantevit, Céline Robardet, Romain Vuillemot

2022-09-19

In Machine learning and data mining for sports analytics - 9th international workshop, MLSA 2022

Abstract

We report on preliminary results to automatically identify efficient tactics of elite players in table tennis games. We define such tactics as subgroups of winning strokes which table tennis experts sought to obtain to train players and adapt their strategy during games. We first report on the creation of such subgroups and their ranking by weighted relative accuracy measure (WRAcc). We then report on representation of the subgroups using visualizations that enabled our expert to provide rapid feedback and hence provided us with guidance towards further improvements of our discoveries

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Improving the quality of rule-based GNN explanations

By Ataollah Kamal, Elouan Vincent, Marc Plantevit, Céline Robardet

2022-09-12

In Workshop on eXplainable knowledge discovery in data mining. Machine learning and principles and practice of knowledge discovery in databases - international workshops of ECML PKDD 2022, grenoble, france, september 19-23, 2022, proceedings, part I

Abstract

Recent works have proposed to explain GNNs using activation rules. Activation rules allow to capture specific configurations in the embedding space of a given layer that is discriminant for the GNN decision. These rules also catch hidden features of input graphs. This requires to associate these rules to representative graphs. In this paper, we propose on the one hand an analysis of heuristic-based algorithms to extract the activation rules, and on the other hand the use of transport-based optimal graph distances to associate each rule with the most specific graph that triggers them.

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