ML Anthology

Ibrahim, Mark

30 publications

ICLR 2025 $\mathbb{X}$-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
NeurIPS 2025 AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, Samuel Bell
NeurIPS 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
TMLR 2025 Occam’s Razor for SSL: Memory-Efficient Parametric Instance Discrimination Eric Gan, Patrik Reizinger, Alice Bizeul, Attila Juhos, Mark Ibrahim, Randall Balestriero, David Klindt, Wieland Brendel, Baharan Mirzasoleiman
NeurIPS 2025 Rethinking the Role of Verbatim Memorization in LLM Privacy Tom Sander, Bargav Jayaraman, Mark Ibrahim, Kamalika Chaudhuri, Chuan Guo
NeurIPS 2025 What’s in Common? Multimodal Models Hallucinate When Reasoning Across Scenes Candace Ross, Florian Bordes, Adina Williams, Polina Kirichenko, Mark Ibrahim
ICMLW 2024 $\mathbb{X}$-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
NeurIPSW 2024 $\mathbb{X}$-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
NeurIPSW 2024 DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features Attila Juhos, Alice Bizeul, Patrik Reizinger, Randall Balestriero, David Klindt, Mark Ibrahim, Julia E Vogt, Wieland Brendel
NeurIPSW 2024 DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features Attila Juhos, Alice Bizeul, Patrik Reizinger, David Klindt, Randall Balestriero, Mark Ibrahim, Julia E Vogt, Wieland Brendel
ICML 2024 Discovering Environments with XRM Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
ICLR 2024 Does Progress on Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data? Megan Richards, Polina Kirichenko, Diane Bouchacourt, Mark Ibrahim
ICML 2024 Modeling Caption Diversity in Contrastive Vision-Language Pretraining Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mido Assran, Andrew Gordon Wilson, Aaron Courville, Nicolas Ballas
NeurIPSW 2024 Occam's Razor for Self Supervised Learning: What Is Sufficient to Learn Good Representations? Mark Ibrahim, David Klindt, Randall Balestriero
ICLRW 2024 The Bias of Harmful Label Associations in Vision-Language Models Caner Hazirbas, Alicia Yi Sun, Yonathan Efroni, Mark Ibrahim
NeurIPS 2024 The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More Ouail Kitouni, Niklas Nolte, Diane Bouchacourt, Adina Williams, Mike Rabbat, Mark Ibrahim
NeurIPS 2024 UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling Haider Al-Tahan, Quentin Garrido, Randall Balestriero, Diane Bouchacourt, Caner Hazirbas, Mark Ibrahim
CVPR 2023 A Whac-a-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim
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 G Wilson, Tom Goldstein
NeurIPSW 2023 Discovering Environments with XRM Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
ICLR 2023 Disentanglement of Correlated Factors via Hausdorff Factorized Support Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt
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
ICLR 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
NeurIPS 2023 PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning Florian Bordes, Shashank Shekhar, Mark Ibrahim, Diane Bouchacourt, Pascal Vincent, Ari Morcos
NeurIPSW 2023 Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Mark Ibrahim, Bernhard Schölkopf
TMLR 2023 The Robustness Limits of SoTA Vision Models to Natural Variation Mark Ibrahim, Quentin Garrido, Ari S. Morcos, Diane Bouchacourt
ICLRW 2023 Understanding the Class-Specific Effects of Data Augmentations Polina Kirichenko, Randall Balestriero, Mark Ibrahim, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson
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 G Wilson
NeurIPS 2021 CrypTen: Secure Multi-Party Computation Meets Machine Learning Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten
NeurIPS 2021 Grounding Inductive Biases in Natural Images: Invariance Stems from Variations in Data Diane Bouchacourt, Mark Ibrahim, Ari Morcos