Connecting every bit of knowledge: The structure of Wikipedia's First Link Network. - Mark Ibrahim; Christopher M. Danforth; Peter Sheridan Dodds (J. Comput. Sci., 2017)

Towards Explainable Deep Learning for Credit Lending: A Case Study. - Ceena Modarres; Mark Ibrahim; Melissa Louie; John W. Paisley (CoRR, 2018)

Mixed Membership Recurrent Neural Networks. - Ghazal Fazelnia; Mark Ibrahim; Ceena Modarres; Kevin Wu; John W. Paisley (CoRR, 2018)

Global Explanations of Neural Networks: Mapping the Landscape of Predictions. - Mark Ibrahim; Melissa Louie; Ceena Modarres; John W. Paisley (AIES, 2019)

Mixed membership recurrent neural networks for modeling customer purchases. - Ghazal Fazelnia; Mark Ibrahim; Ceena Modarres; Kevin Wu; John W. Paisley (ICAIF, 2020)

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

CrypTen: Secure Multi-Party Computation Meets Machine Learning. - Brian Knott; Shobha Venkataraman; Awni Y. Hannun; Shubho Sengupta; Mark Ibrahim; Laurens van der Maaten (NeurIPS, 2021)

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

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

RidgeSketch: A Fast Sketching Based Solver for Large Scale Ridge Regression. - Nidham Gazagnadou; Mark Ibrahim; Robert M. Gower (SIAM J. Matrix Anal. Appl., 2022)

A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others. - Zhiheng Li 002; Ivan Evtimov; Albert Gordo; Caner Hazirbas; Tal Hassner; Cristian Canton-Ferrer; Chenliang Xu; Mark Ibrahim (CVPR, 2023)

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)

VPA: Fully Test-Time Visual Prompt Adaptation. - Jiachen Sun; Mark Ibrahim; Melissa Hall; Ivan Evtimov; Z. Morley Mao; Cristian Canton-Ferrer; Caner Hazirbas (ACM Multimedia, 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)

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. - Micah Goldblum; Hossein Souri; Renkun Ni; Manli Shu; Viraj Prabhu; Gowthami Somepalli; Prithvijit Chattopadhyay; Mark Ibrahim; Adrien Bardes; Judy Hoffman; Rama Chellappa; Andrew Gordon Wilson; et al. (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)

A Cookbook of Self-Supervised Learning. - Randall Balestriero; Mark Ibrahim; Vlad Sobal; Ari Morcos; Shashank Shekhar; Tom Goldstein; Florian Bordes; Adrien Bardes; Grégoire Mialon; Yuandong Tian; Avi Schwarzschild; Andrew Gordon Wilson; et al. (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)

WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models. - Youssef Benchekroun; Megi Dervishi; Mark Ibrahim; Jean-Baptiste Gaya; Xavier Martinet; Grégoire Mialon; Thomas Scialom; Emmanuel Dupoux; Dieuwke Hupkes; Pascal Vincent (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)

Modeling Caption Diversity in Contrastive Vision-Language Pretraining. - Samuel Lavoie; Polina Kirichenko; Mark Ibrahim; Mido Assran; Andrew Gordon Wilson; Aaron C. Courville; Nicolas Ballas (ICML, 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)

Prognostic value of coronary artery calcium scoring in patients with non-small cell lung cancer using initial staging computed tomography. - Aryan Zahergivar; Mahshid Golagha; Greg Stoddard; Parker Sage Anderson; Lacey Woods; Anna Newman; Malorie R. Carter; Libo Wang; Mark Ibrahim; Jordan Chamberlin; William F. Auffermann; Ismail Kabakus; et al. (BMC Medical Imaging, 2024)

The Bias of Harmful Label Associations in Vision-Language Models. - Caner Hazirbas; Alicia Sun; Yonathan Efroni; Mark Ibrahim (CoRR, 2024)

Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. - Christian Tomani; Kamalika Chaudhuri; Ivan Evtimov; Daniel Cremers; Mark Ibrahim (CoRR, 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)

Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations? - Mark Ibrahim; David A. Klindt; Randall Balestriero (CoRR, 2024)

Transformers Can Navigate Mazes With Multi-Step Prediction. - Niklas Nolte; Ouail Kitouni; Adina Williams; Mike Rabbat; Mark Ibrahim (CoRR, 2024)

EvalGIM: A Library for Evaluating Generative Image Models. - Melissa Hall; Oscar Mañas; Reyhane Askari Hemmat; Mark Ibrahim; Candace Ross; Pietro Astolfi; Tariq Berrada Ifriqi; Marton Havasi; Yohann Benchetrit; Karen Ullrich; Carolina Braga; Abhishek Charnalia; et al. (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)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning. - Hugues Van Assel; Mark Ibrahim; Tommaso Biancalani; Aviv Regev; Randall Balestriero (CoRR, 2025)

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions. - Polina Kirichenko; Mark Ibrahim; Kamalika Chaudhuri; Samuel J. Bell (CoRR, 2025)