Certainty Pooling for Multiple Instance Learning. - Jacob Gildenblat; Ido Ben-Shaul; Zvi Lapp; Eldad Klaiman (ICPR Workshops (1), 2020)

Solving the functional Eigen-Problem using Neural Networks. - Ido Ben-Shaul; Leah Bar; Nir A. Sochen (CoRR, 2020)

Sparsity-Probe: Analysis tool for Deep Learning Models. - Ido Ben-Shaul; Shai Dekel (CoRR, 2021)

Is it out yet? Automatic Future Product Releases Extraction from Web Data. - Gilad Fuchs; Ido Ben-Shaul; Matan Mandelbrod (EMNLP (Industry Track), 2022)

Nearest Class-Center Simplification through Intermediate Layers. - Ido Ben-Shaul; Shai Dekel (TAG-ML, 2022)

Pricing the Nearly Known - When Semantic Similarity is Just not Enough. - Gilad Fuchs; Pavel Petrov; Ido Ben-Shaul; Matan Mandelbrod; Oded Zinman; Dmitry Basin; Vadim Arshavsky (eCom@SIGIR, 2023)

Reverse Engineering Self-Supervised Learning. - Ido Ben-Shaul; Ravid Shwartz-Ziv; Tomer Galanti; Shai Dekel; Yann LeCun (NeurIPS, 2023)

Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions. - Ido Ben-Shaul; Tomer Galanti; Shai Dekel (Trans. Mach. Learn. Res., 2023)

Comparative Generalization Bounds for Deep Neural Networks. - Tomer Galanti; Liane Galanti; Ido Ben-Shaul (Trans. Mach. Learn. Res., 2023)

Deep Learning Solution of the Eigenvalue Problem for Differential Operators. - Ido Ben-Shaul; Leah Bar; Dalia Fishelov; Nir A. Sochen (Neural Comput., 2023)

Artificial Expert Intelligence through PAC-reasoning. - Shai Shalev-Shwartz; Amnon Shashua; Gal Beniamini; Yoav Levine; Or Sharir; Noam Wies; Ido Ben-Shaul; Tomer Nussbaum; Shir Granot Peled (CoRR, 2024)

Practical Fact Checking System for LLMs. - Gilad Fuchs; Oded Zinman; Ido Ben-Shaul (WWW (Companion Volume), 2025)

FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming. - Gal Beniamini; Yuval Dor; Alon Vinnikov; Shir Granot Peled; Or Weinstein; Or Sharir; Noam Wies; Tomer Nussbaum; Ido Ben-Shaul; Tomer Zekharya; Yoav Levine; Shai Shalev-Shwartz; et al. (CoRR, 2025)