Entropy-Based Latent Structured Output Prediction. - Diane Bouchacourt; Sebastian Nowozin; M. Pawan Kumar (ICCV, 2015)

DISCO Nets : DISsimilarity COefficients Networks. - Diane Bouchacourt; Pawan Kumar Mudigonda; Sebastian Nowozin (NIPS, 2016)

DISCO Nets: DISsimilarity COefficient Networks. - Diane Bouchacourt; M. Pawan Kumar; Sebastian Nowozin (CoRR, 2016)

Task-oriented learning of structured probability distributions. - Diane Bouchacourt (2017)

Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations. - Diane Bouchacourt; Ryota Tomioka; Sebastian Nowozin (AAAI, 2018)

How agents see things: On visual representations in an emergent language game. - Diane Bouchacourt; Marco Baroni (EMNLP, 2018)

Miss Tools and Mr Fruit: Emergent Communication in Agents Learning about Object Affordances. - Diane Bouchacourt; Marco Baroni (ACL (1), 2019)

EGG: a toolkit for research on Emergence of lanGuage in Games. - Eugene Kharitonov; Rahma Chaabouni 001; Diane Bouchacourt; Marco Baroni (EMNLP/IJCNLP (3), 2019)

EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction. - Diane Bouchacourt; Ludovic Denoyer (CoRR, 2019)

Information Minimization In Emergent Languages. - Eugene Kharitonov; Rahma Chaabouni 001; Diane Bouchacourt; Marco Baroni (CoRR, 2019)

Mastering emergent language: learning to guide in simulated navigation. - Mathijs Mul; Diane Bouchacourt; Elia Bruni (CoRR, 2019)

Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game. - Roberto Dessì; Diane Bouchacourt; Davide Crepaldi; Marco Baroni (CoRR, 2019)

Compositionality and Generalization In Emergent Languages. - Rahma Chaabouni 001; Eugene Kharitonov; Diane Bouchacourt; Emmanuel Dupoux; Marco Baroni (ACL, 2020)

Permutation Equivariant Models for Compositional Generalization in Language. - Jonathan Gordon 003; David Lopez-Paz; Marco Baroni; Diane Bouchacourt (ICLR, 2020)

Entropy Minimization In Emergent Languages. - Eugene Kharitonov; Rahma Chaabouni 001; Diane Bouchacourt; Marco Baroni (ICML, 2020)

A Benchmark for Systematic Generalization in Grounded Language Understanding. - Laura Ruis; Jacob Andreas; Marco Baroni; Diane Bouchacourt; Brenden M. Lake (NeurIPS, 2020)

Think before you act: A simple baseline for compositional generalization. - Christina Heinze-Deml; Diane Bouchacourt (CoRR, 2020)

Grounding inductive biases in natural images: invariance stems from variations in data. - Diane Bouchacourt; Mark Ibrahim; Ari S. Morcos (NeurIPS, 2021)

Addressing the Topological Defects of Disentanglement via Distributed Operators. - Diane Bouchacourt; Mark Ibrahim; Stéphane Deny (CoRR, 2021)

Measuring and signing fairness as performance under multiple stakeholder distributions. - David Lopez-Paz; Diane Bouchacourt; Levent Sagun; Nicolas Usunier (CoRR, 2022)

Robust Self-Supervised Learning with Lie Groups. - Mark Ibrahim; Diane Bouchacourt; Ari S. Morcos (CoRR, 2022)

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. - Badr Youbi Idrissi; Diane Bouchacourt; Randall Balestriero; Ivan Evtimov; Caner Hazirbas; Nicolas Ballas; Pascal Vincent; Michal Drozdzal; David Lopez-Paz; Mark Ibrahim (ICLR, 2023)

Disentanglement of Correlated Factors via Hausdorff Factorized Support. - Karsten Roth; Mark Ibrahim; Zeynep Akata; Pascal Vincent; Diane Bouchacourt (ICLR, 2023)

Birth of a Transformer: A Memory Viewpoint. - Alberto Bietti; Vivien Cabannes; Diane Bouchacourt; Hervé Jégou; Léon Bottou (NeurIPS, 2023)

PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning. - Florian Bordes; Shashank Shekhar; Mark Ibrahim; Diane Bouchacourt; Pascal Vincent; Ari Morcos (NeurIPS, 2023)

Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations. - Laura Gustafson; Megan Richards; Melissa Hall; Caner Hazirbas; Diane Bouchacourt; Mark Ibrahim (NeurIPS, 2023)

Understanding the detrimental class-level effects of data augmentation. - Polina Kirichenko; Mark Ibrahim; Randall Balestriero; Diane Bouchacourt; Shanmukha Ramakrishna Vedantam; Hamed Firooz; Andrew Gordon Wilson (NeurIPS, 2023)

Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies. - Laura Gustafson; Megan Richards; Melissa Hall; Caner Hazirbas; Diane Bouchacourt; Mark Ibrahim (CoRR, 2023)

Does Progress On Object Recognition Benchmarks Improve Real-World Generalization? - Megan Richards; Polina Kirichenko; Diane Bouchacourt; Mark Ibrahim (CoRR, 2023)

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations. - Cian Eastwood; Julius von Kügelgen; Linus Ericsson; Diane Bouchacourt; Pascal Vincent; Bernhard Schölkopf; Mark Ibrahim (CoRR, 2023)

The Robustness Limits of SoTA Vision Models to Natural Variation. - Mark Ibrahim; Quentin Garrido; Ari S. Morcos; Diane Bouchacourt (Trans. Mach. Learn. Res., 2023)

Embracing Diversity: Interpretable Zero-shot Classification Beyond One Vector Per Class. - Mazda Moayeri; Michael Rabbat; Mark Ibrahim; Diane Bouchacourt (FAccT, 2024)

Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data? - Megan Richards; Polina Kirichenko; Diane Bouchacourt; Mark Ibrahim (ICLR, 2024)

Discovering Environments with XRM. - Mohammad Pezeshki; Diane Bouchacourt; Mark Ibrahim; Nicolas Ballas; Pascal Vincent; David Lopez-Paz (ICML, 2024)

UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling. - Haider Al-Tahan; Quentin Garrido; Randall Balestriero; Diane Bouchacourt; Caner Hazirbas; Mark Ibrahim (NeurIPS, 2024)

The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More. - Ouail Kitouni; Niklas Nolte; Adina Williams; Michael Rabbat; Diane Bouchacourt; Mark Ibrahim (NeurIPS, 2024)

An Introduction to Vision-Language Modeling. - Florian Bordes; Richard Yuanzhe Pang; Anurag Ajay; Alexander C. Li; Adrien Bardes; Suzanne Petryk; Oscar Mañas; Zhiqiu Lin; Anas Mahmoud 002; Bargav Jayaraman; Mark Ibrahim; Melissa Hall; et al. (CoRR, 2024)

Reassessing the Validity of Spurious Correlations Benchmarks. - Samuel J. Bell; Diane Bouchacourt; Levent Sagun (CoRR, 2024)

X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs. - Vlad Sobal; Mark Ibrahim; Randall Balestriero; Vivien Cabannes; Diane Bouchacourt; Pietro Astolfi; Kyunghyun Cho; Yann LeCun (ICLR, 2025)