Gas to Olive Oil Partition Coefficients: A Linear Free Energy Analysis. - Michael H. Abraham; Adam Ibrahim (J. Chem. Inf. Model., 2006)

Predicting Penetration Across the Blood-Brain Barrier from Simple Descriptors and Fragmentation Schemes. - Yuan H. Zhao; Michael H. Abraham; Adam Ibrahim; Paul V. Fish; Susan Cole; Mark L. Lewis; Marcel J. de Groot; Derek P. Reynolds (J. Chem. Inf. Model., 2007)

ARbis Pictus: A Study of Language Learning with Augmented Reality. - Adam Ibrahim; Brandon Huynh; Jonathan Downey; Tobias Höllerer; Dorothy Chun; John O'Donovan (CoRR, 2017)

A Study of Situated Product Recommendations in Augmented Reality. - Brandon Huynh; Adam Ibrahim; YunSuk Chang; Tobias Höllerer; John O'Donovan (AIVR, 2018)

ARbis Pictus: A Study of Vocabulary Learning with Augmented Reality. - Adam Ibrahim; Brandon Huynh; Jonathan Downey; Tobias Höllerer; Dorothy Chun; John O'Donovan (IEEE Trans. Vis. Comput. Graph., 2018)

Lower Bounds and Conditioning of Differentiable Games. - Adam Ibrahim; Waïss Azizian; Gauthier Gidel; Ioannis Mitliagkas (CoRR, 2019)

User Perception of Situated Product Recommendations in Augmented Reality. - Brandon Huynh; Adam Ibrahim; YunSuk Chang; Tobias Höllerer; John O'Donovan (Int. J. Semantic Comput., 2019)

Linear Lower Bounds and Conditioning of Differentiable Games. - Adam Ibrahim; Waïss Azizian; Gauthier Gidel; Ioannis Mitliagkas (ICML, 2020)

Adversarial Feature Desensitization. - Pouya Bashivan; Reza Bayat; Adam Ibrahim; Kartik Ahuja; Mojtaba Faramarzi; Touraj Laleh; Blake A. Richards; Irina Rish (NeurIPS, 2021)

Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound. - Charles Guille-Escuret; Adam Ibrahim; Baptiste Goujaud; Ioannis Mitliagkas (NeurIPS, 2022)

Learning Robust Kernel Ensembles with Kernel Average Pooling. - Pouya Bashivan; Adam Ibrahim; Amirozhan Dehghani; Yifei Ren (CoRR, 2022)

Towards Out-of-Distribution Adversarial Robustness. - Adam Ibrahim; Charles Guille-Escuret; Ioannis Mitliagkas; Irina Rish; David Krueger 001; Pouya Bashivan (CoRR, 2022)

Continual Pre-Training of Large Language Models: How to (re)warm your model? - Kshitij Gupta; Benjamin Thérien; Adam Ibrahim; Mats L. Richter; Quentin Anthony; Eugene Belilovsky; Irina Rish; Timothée Lesort (CoRR, 2023)

Zamba: A Compact 7B SSM Hybrid Model. - Paolo Glorioso; Quentin Anthony; Yury Tokpanov; James Whittington; Jonathan Pilault; Adam Ibrahim; Beren Millidge (CoRR, 2024)

Zyda: A 1.3T Dataset for Open Language Modeling. - Yury Tokpanov; Beren Millidge; Paolo Glorioso; Jonathan Pilault; Adam Ibrahim; James Whittington; Quentin Anthony (CoRR, 2024)

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive? - Rylan Schaeffer; Hailey Schoelkopf; Brando Miranda; Gabriel Mukobi; Varun Madan; Adam Ibrahim; Herbie Bradley; Stella Biderman; Sanmi Koyejo (CoRR, 2024)

Simple and Scalable Strategies to Continually Pre-train Large Language Models. - Adam Ibrahim; Benjamin Thérien; Kshitij Gupta; Mats L. Richter; Quentin Gregory Anthony; Eugene Belilovsky; Timothée Lesort; Irina Rish (Trans. Mach. Learn. Res., 2024)

Learning adversarially robust kernel ensembles with kernel average pooling. - Pouya Bashivan; Reza Bayat; Adam Ibrahim; Amirozhan Dehghani; Yifei Ren (Expert Syst. Appl., 2025)

Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training. - Vaibhav Singh; Paul Janson; Paria Mehrbod; Adam Ibrahim; Irina Rish; Eugene Belilovsky; Benjamin Thérien (CoRR, 2025)