Disrupting Deep Uncertainty Estimation Without Harming Accuracy. - Ido Galil; Ran El-Yaniv (NeurIPS, 2021)

Which models are innately best at uncertainty estimation? - Ido Galil; Mohammed Dabbah; Ran El-Yaniv (CoRR, 2022)

A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. - Ido Galil; Mohammed Dabbah; Ran El-Yaniv (ICLR, 2023)

What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? - Ido Galil; Mohammed Dabbah; Ran El-Yaniv (ICLR, 2023)

Hierarchical Selective Classification. - Shani Goren; Ido Galil; Ran El-Yaniv (NeurIPS, 2024)

Puzzle: Distillation-Based NAS for Inference-Optimized LLMs. - Akhiad Bercovich; Tomer Ronen; Talor Abramovich; Nir Ailon; Nave Assaf; Mohammad Dabbah; Ido Galil; Amnon Geifman; Yonatan Geifman; Izhak Golan; Netanel Haber; Ehud Karpas; et al. (CoRR, 2024)

Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models. - Michael Toker; Ido Galil; Hadas Orgad; Rinon Gal; Yoad Tewel; Gal Chechik; Yonatan Belinkov (NAACL (Long Papers), 2025)

No Data, No Optimization: A Lightweight Method To Disrupt Neural Networks With Sign-Flips. - Ido Galil; Moshe Kimhi; Ran El-Yaniv (CoRR, 2025)

FFN Fusion: Rethinking Sequential Computation in Large Language Models. - Akhiad Bercovich; Mohammad Dabbah; Omri Puny; Ido Galil; Amnon Geifman; Yonatan Geifman; Izhak Golan; Ehud Karpas; Itay Levy; Zach Moshe; Najeeb Nabwani; Tomer Ronen; et al. (CoRR, 2025)