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Matthieu Labeau

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5 papers

2 author rows

ICML Conference 2025 Conference Paper

To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers

Roman Plaud
Alexandre Perez-Lebel
Matthieu Labeau
Antoine Saillenfest
Thomas Bonald

Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings frequently relies on heuristic decision rules, which may not align with task-specific evaluation metrics. In this work, we propose a framework for the optimal decoding of an output probability distribution with respect to a target metric. We derive optimal decision rules for increasingly complex prediction settings, providing universal algorithms when candidates are limited to the set of nodes. In the most general case of predicting a subset of nodes, we focus on rules dedicated to the hierarchical $\mathrm{hF}_{\beta}$ scores, tailored to hierarchical settings. To demonstrate the practical utility of our approach, we conduct extensive empirical evaluations, showcasing the superiority of our proposed optimal strategies, particularly in underdetermined scenarios. These results highlight the potential of our methods to enhance the performance and reliability of hierarchical classifiers in real-world applications.

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NeurIPS Conference 2024 Conference Paper

Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss

Paul Krzakala
Junjie Yang
Rémi Flamary
Florence d'Alché-Buc
Charlotte Laclau
Matthieu Labeau

We propose Any2graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i. e. a deep learning model that predicts an entire graph for any kind of input. The framework is built on a novel Optimal Transport loss, the Partially-Masked Fused Gromov-Wasserstein, that exhibits all necessary properties (permutation invariance, differentiability and scalability) and is designed to handle any-sized graphs. Numerical experiments showcase the versatility of the approach that outperform existing competitors on a novel challenging synthetic dataset and a variety of real-world tasks such as map construction from satellite image (Sat2Graph) or molecule prediction from fingerprint (Fingerprint2Graph).

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TMLR Journal 2024 Journal Article

Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance

Junjie Yang
Matthieu Labeau
Florence d'Alché-Buc

Pairwise comparison of graphs is key to many applications in Machine Learning ranging from clustering, kernel-based classification/regression and more recently supervised graph prediction. Distances between graphs usually rely on informative representations of these structured objects such as bag of substructures or other graph embeddings. A recently popular solution consists in representing graphs as metric measure spaces, allowing to successfully leverage Optimal Transport, which provides meaningful distances allowing to compare them, namely the Gromov-Wasserstein distance and its variant the Fused Gromov-Wasserstein that applies on node attributed graphs. However, this family of distances overlooks edge attributes, which are essential for many structured objects. In this work, we introduce an extension of the Fused Gromov-Wasserstein distance for comparing graphs whose both nodes and edges have features. We propose novel algorithms for distance and barycenter computation. We present a range of studies that illustrate the properties of the proposed distance and empirically demonstrate its effectiveness in supervised graph prediction tasks.

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AAAI Conference 2023 Conference Paper

An Adaptive Layer to Leverage Both Domain and Task Specific Information from Scarce Data

Gaël Guibon
Matthieu Labeau
Luce Lefeuvre
Chloé Clavel

Many companies make use of customer service chats to help the customer and try to solve their problem. However, customer service data is confidential and as such, cannot easily be shared in the research community. This also implies that these data are rarely labeled, making it difficult to take advantage of it with machine learning methods. In this paper we present the first work on a customer’s problem status prediction and identification of problematic conversations. Given very small subsets of labeled textual conversations and unlabeled ones, we propose a semi-supervised framework dedicated to customer service data leveraging speaker role information to adapt the model to the domain and the task using a two-step process. Our framework, Task-Adaptive Fine-tuning, goes from predicting customer satisfaction to identifying the status of the customer’s problem, with the latter being the main objective of the multi-task setting. It outperforms recent inductive semi-supervised approaches on this novel task while only considering a relatively low number of parameters to train on during the final target task. We believe it can not only serve models dedicated to customer service but also to any other application making use of confidential conversational data where labeled sets are rare. Source code is available at https://github.com/gguibon/taft

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ICLR Conference 2020 Conference Paper

Compositional languages emerge in a neural iterated learning model

Yi Ren
Shangmin Guo
Matthieu Labeau
Shay B. Cohen
Simon Kirby

The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set of messages using a limited vocabulary. If compositionality is indeed a natural property of language, we may expect it to appear in communication protocols that are created by neural agents via grounded language learning. Inspired by the iterated learning framework, which simulates the process of language evolution, we propose an effective neural iterated learning algorithm that, when applied to interacting neural agents, facilitates the emergence of a more structured type of language. Indeed, these languages provide specific advantages to neural agents during training, which translates as a larger posterior probability, which is then incrementally amplified via the iterated learning procedure. Our experiments confirm our analysis, and also demonstrate that the emerged languages largely improve the generalization of the neural agent communication.

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