Support vector machines with indefinite kernels. - Ibrahim M. Alabdulmohsin; Xin Gao 001; Xiangliang Zhang 001 (ACML, 2014)

Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering. - Ibrahim M. Alabdulmohsin; Xin Gao 001; Xiangliang Zhang 001 (CIKM, 2014)

Interference in wireless ad hoc networks with smart antennas. - Ibrahim M. Alabdulmohsin (IWCMC, 2014)

End-to-end delay analysis in wireless sensor networks with service vacation. - Ibrahim M. Alabdulmohsin; Amal Hyadi; Laila H. Afify; Basem Shihada (WCNC, 2014)

A Mathematical Theory of Learning. - Ibrahim M. Alabdulmohsin (CoRR, 2014)

Efficient Active Learning of Halfspaces via Query Synthesis. - Ibrahim M. Alabdulmohsin; Xin Gao 001; Xiangliang Zhang 001 (AAAI, 2015)

Algorithmic Stability and Uniform Generalization. - Ibrahim M. Alabdulmohsin (NIPS, 2015)

Content-Agnostic Malware Detection in Heterogeneous Malicious Distribution Graph. - Ibrahim M. Alabdulmohsin; Yufei Han 001; Yun Shen; Xiangliang Zhang 001 (CIKM, 2016)

Is Attribute-Based Zero-Shot Learning an Ill-Posed Strategy? - Ibrahim M. Alabdulmohsin; Moustapha Cissé; Xiangliang Zhang 001 (ECML/PKDD (1), 2016)

Uniform Generalization, Concentration, and Adaptive Learning. - Ibrahim M. Alabdulmohsin (CoRR, 2016)

Large margin classification with indefinite similarities. - Ibrahim M. Alabdulmohsin; Moustapha Cissé; Xin Gao 001; Xiangliang Zhang 001 (Mach. Learn., 2016)

An Information-Theoretic Route from Generalization in Expectation to Generalization in Probability. - Ibrahim M. Alabdulmohsin (AISTATS, 2017)

Learning via Query Synthesis. - Ibrahim Alabdulmohsin (2017)

Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization. - Ibrahim M. Alabdulmohsin (ICML, 2018)

Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure. - Ibrahim M. Alabdulmohsin (ECML/PKDD (1), 2018)

What Do Neural Networks Learn When Trained With Random Labels? - Hartmut Maennel; Ibrahim M. Alabdulmohsin; Ilya O. Tolstikhin; Robert J. N. Baldock; Olivier Bousquet; Sylvain Gelly; Daniel Keysers (NeurIPS, 2020)

Fair Classification via Unconstrained Optimization. - Ibrahim M. Alabdulmohsin (CoRR, 2020)

Towards a Unified Theory of Learning and Information. - Ibrahim M. Alabdulmohsin (Entropy, 2020)

A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models. - Ibrahim M. Alabdulmohsin; Mario Lucic (NeurIPS, 2021)

A Generalization of Classical Formulas in Numerical Integration and Series Convergence Acceleration. - Ibrahim M. Alabdulmohsin (CoRR, 2021)

A Generalized Lottery Ticket Hypothesis. - Ibrahim M. Alabdulmohsin; Larisa Markeeva; Daniel Keysers; Ilya O. Tolstikhin (CoRR, 2021)

The Impact of Reinitialization on Generalization in Convolutional Neural Networks. - Ibrahim M. Alabdulmohsin; Hartmut Maennel; Daniel Keysers (CoRR, 2021)

Revisiting Neural Scaling Laws in Language and Vision. - Ibrahim M. Alabdulmohsin; Behnam Neyshabur; Xiaohua Zhai (NeurIPS, 2022)

A Reduction to Binary Approach for Debiasing Multiclass Datasets. - Ibrahim M. Alabdulmohsin; Jessica Schrouff; Sanmi Koyejo (NeurIPS, 2022)

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. - Jessica Schrouff; Natalie Harris; Sanmi Koyejo; Ibrahim M. Alabdulmohsin; Eva Schnider; Krista Opsahl-Ong; Alexander Brown; Subhrajit Roy; Diana Mincu; Christina Chen; Awa Dieng; Yuan Liu; et al. (NeurIPS, 2022)

Fair Wrapping for Black-box Predictions. - Alexander Soen; Ibrahim M. Alabdulmohsin; Sanmi Koyejo; Yishay Mansour; Nyalleng Moorosi; Richard Nock; Ke Sun 001; Lexing Xie (NeurIPS, 2022)

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications? - Jessica Schrouff; Natalie Harris; Oluwasanmi Koyejo; Ibrahim Alabdulmohsin; Eva Schnider; Krista Opsahl-Ong; Alexander Brown; Subhrajit Roy; Diana Mincu; Christina Chen; Awa Dieng; Yuan Liu; et al. (CoRR, 2022)

Layer-Stack Temperature Scaling. - Amr Khalifa; Michael C. Mozer; Hanie Sedghi; Behnam Neyshabur; Ibrahim Alabdulmohsin (CoRR, 2022)

Adapting to Latent Subgroup Shifts via Concepts and Proxies. - Ibrahim Alabdulmohsin; Nicole Chiou; Alexander D'Amour; Arthur Gretton; Sanmi Koyejo; Matt J. Kusner; Stephen R. Pfohl; Olawale Salaudeen; Jessica Schrouff; Katherine Tsai (AISTATS, 2023)

FlexiViT: One Model for All Patch Sizes. - Lucas Beyer; Pavel Izmailov; Alexander Kolesnikov 003; Mathilde Caron; Simon Kornblith; Xiaohua Zhai; Matthias Minderer; Michael Tschannen; Ibrahim Alabdulmohsin; Filip Pavetic (CVPR, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. - Mostafa Dehghani 001; Basil Mustafa; Josip Djolonga; Jonathan Heek; Matthias Minderer; Mathilde Caron; Andreas Steiner 001; Joan Puigcerver; Robert Geirhos; Ibrahim M. Alabdulmohsin; Avital Oliver; Piotr Padlewski; et al. (NeurIPS, 2023)

Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design. - Ibrahim M. Alabdulmohsin; Xiaohua Zhai; Alexander Kolesnikov 003; Lucas Beyer (NeurIPS, 2023)

PaLI-X: On Scaling up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CoRR, 2023)

PaLI-3 Vision Language Models: Smaller, Faster, Stronger. - Xi Chen 071; Xiao Wang 038; Lucas Beyer; Alexander Kolesnikov 003; Jialin Wu; Paul Voigtlaender; Basil Mustafa; Sebastian Goodman; Ibrahim Alabdulmohsin; Piotr Padlewski; Daniel Salz; Xi Xiong; et al. (CoRR, 2023)

On Scaling Up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CVPR, 2024)

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning? - Ibrahim Alabdulmohsin; Xiao Wang 038; Andreas Peter Steiner; Priya Goyal; Alexander D'Amour; Xiaohua Zhai (ICLR, 2024)

Fractal Patterns May Illuminate the Success of Next-Token Prediction. - Ibrahim M. Alabdulmohsin; Vinh Q. Tran 002; Mostafa Dehghani 001 (NeurIPS, 2024)

No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models. - Angéline Pouget; Lucas Beyer; Emanuele Bugliarello; Xiao Wang 038; Andreas Steiner 001; Xiaohua Zhai; Ibrahim M. Alabdulmohsin (NeurIPS, 2024)

LocCa: Visual Pretraining with Location-aware Captioners. - Bo Wan; Michael Tschannen; Yongqin Xian; Filip Pavetic; Ibrahim M. Alabdulmohsin; Xiao Wang 038; André Susano Pinto; Andreas Steiner 001; Lucas Beyer; Xiaohua Zhai (NeurIPS, 2024)

Fractal Patterns May Unravel the Intelligence in Next-Token Prediction. - Ibrahim Alabdulmohsin; Vinh Q. Tran 002; Mostafa Dehghani 001 (CoRR, 2024)

PaliGemma: A versatile 3B VLM for transfer. - Lucas Beyer; Andreas Steiner 001; André Susano Pinto; Alexander Kolesnikov 003; Xiao Wang 038; Daniel Salz; Maxim Neumann; Ibrahim Alabdulmohsin; Michael Tschannen; Emanuele Bugliarello; Thomas Unterthiner; Daniel Keysers; et al. (CoRR, 2024)

PaliGemma 2: A Family of Versatile VLMs for Transfer. - Andreas Steiner 001; André Susano Pinto; Michael Tschannen; Daniel Keysers; Xiao Wang 038; Yonatan Bitton; Alexey A. Gritsenko; Matthias Minderer; Anthony Sherbondy; Shangbang Long; Siyang Qin; R. Reeve Ingle; et al. (CoRR, 2024)

Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems. - Ibrahim Alabdulmohsin; Xiaohua Zhai (CoRR, 2025)

Scaling Pre-training to One Hundred Billion Data for Vision Language Models. - Xiao Wang 038; Ibrahim Alabdulmohsin; Daniel Salz; Zhe Li; Keran Rong; Xiaohua Zhai (CoRR, 2025)

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features. - Michael Tschannen; Alexey A. Gritsenko; Xiao Wang 038; Muhammad Ferjad Naeem; Ibrahim Alabdulmohsin; Nikhil Parthasarathy; Talfan Evans; Lucas Beyer; Ye Xia; Basil Mustafa; Olivier J. Hénaff; Jeremiah Harmsen; et al. (CoRR, 2025)

A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language? - Ibrahim Alabdulmohsin; Andreas Steiner 001 (CoRR, 2025)