Gaussier, Eric

23 publications

TMLR 2026 GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifiers Eloi Zablocki, Valentin Gerard, Amaia Cardiel, Eric Gaussier, Matthieu Cord, Eduardo Valle

UAI 2025 Complete Characterization for Adjustment in Summary Causal Graphs of Time Series Clément Yvernes, Emilie Devijver, Eric Gaussier

NeurIPS 2025 Relaxing Partition Admissibility in Cluster-DAGs: A Causal Calculus with Arbitrary Variable Clustering Clément Yvernes, Emilie Devijver, Adèle H. Ribeiro, Marianne Clausel, Eric Gaussier

TMLR 2024 Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms Daria Bystrova, Charles K. Assaad, Julyan Arbel, Emilie Devijver, Eric Gaussier, Wilfried Thuiller

UAI 2024 Identifiability of Total Effects from Abstractions of Time Series Causal Graphs Charles K. Assaad, Emilie Devijver, Eric Gaussier, Gregor Goessler, Anouar Meynaoui

IJCAI 2023 Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract) Charles K. Assaad, Emilie Devijver, Éric Gaussier

UAI 2022 Discovery of Extended Summary Graphs in Time Series Charles K. Assaad, Emilie Devijver, Eric Gaussier

AAAI 2022 Listwise Learning to Rank Based on Approximate Rank Indicators Thibaut Thonet, Yagmur Gizem Cinar, Éric Gaussier, Minghan Li, Jean-Michel Renders

JAIR 2022 Survey and Evaluation of Causal Discovery Methods for Time Series Charles K. Assaad, Emilie Devijver, Éric Gaussier

ECML-PKDD 2021 A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir

NeurIPS 2020 Heavy-Tailed Representations, Text Polarity Classification & Data Augmentation Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Eric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin

UAI 2020 Mixed-Membership Stochastic Block Models for Weighted Networks Adrien Dulac, Eric Gaussier, Christine Largeron

NeurIPS 2020 Smooth and Consistent Probabilistic Regression Trees Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Eric Gaussier, Georges Oppenheim

JAIR 2019 On Inductive Abilities of Latent Factor Models for Relational Learning Théo Trouillon, Éric Gaussier, Christopher R. Dance, Guillaume Bouchard

JMLR 2017 Knowledge Graph Completion via Complex Tensor Factorization Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, Guillaume Bouchard

ICML 2016 Complex Embeddings for Simple Link Prediction Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard

JMLR 2016 Learning Taxonomy Adaptation in Large-Scale Classification Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cécile Amblard

ICLR 2015 Algorithmic Robustness for Semi-Supervised (ε, Γ, Τ)-Good Metric Learning Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini

AAAI 2015 Improved Local Search for Binary Matrix Factorization Seyed Hamid Mirisaee, Éric Gaussier, Alexandre Termier

ECML-PKDD 2015 Joint Semi-Supervised Similarity Learning for Linear Classification Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban

NeurIPS 2013 On Flat Versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih R. Amini

ICML 2006 Categorization in Multiple Category Systems Jean-Michel Renders, Éric Gaussier, Cyril Goutte, François Pacull, Gabriela Csurka

ECML-PKDD 2006 Revisiting Fisher Kernels for Document Similarities Martin Nyffenegger, Jean-Cédric Chappelier, Éric Gaussier