Yehudai, Gilad

24 publications

NeurIPS 2025 Compositional Reasoning with Transformers, RNNs, and Chain of Thought Gilad Yehudai, Noah Amsel, Joan Bruna

NeurIPS 2025 Depth-Width Tradeoffs for Transformers on Graph Tasks Gilad Yehudai, Clayton Sanford, Maya Bechler-Speicher, Orr Fischer, Ran Gilad-Bachrach, Amir Globerson

NeurIPS 2025 Emergence of Linear Truth Encodings in Language Models Shauli Ravfogel, Gilad Yehudai, Tal Linzen, Joan Bruna, Alberto Bietti

AISTATS 2025 Locally Optimal Descent for Dynamic Stepsize Scheduling Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain

COLT 2025 Logarithmic Width Suffices for Robust Memorization Amitsour Egosi, Gilad Yehudai, Ohad Shamir

ICLR 2025 Quality over Quantity in Attention Layers: When Adding More Heads Hurts Noah Amsel, Gilad Yehudai, Joan Bruna

NeurIPS 2024 MALT Powers up Adversarial Attacks Odelia Melamed, Gilad Yehudai, Adi Shamir

NeurIPSW 2024 On the Reconstruction of Training Data from Group Invariant Networks Ran Elbaz, Gilad Yehudai, Meirav Galun, Haggai Maron

ALT 2024 RedEx: Beyond Fixed Representation Methods via Convex Optimization Amit Daniely, Mariano Schain, Gilad Yehudai

NeurIPS 2023 Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces Odelia Melamed, Gilad Yehudai, Gal Vardi

NeurIPS 2023 Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Yakir Oz, Yaniv Nikankin, Michal Irani

NeurIPS 2023 From Tempered to Benign Overfitting in ReLU Neural Networks Guy Kornowski, Gilad Yehudai, Ohad Shamir

ICLRW 2023 Reconstructing Training Data from Multiclass Neural Networks Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Michal Irani

NeurIPS 2022 Gradient Methods Provably Converge to Non-Robust Networks Gal Vardi, Gilad Yehudai, Ohad Shamir

ICLR 2022 On the Optimal Memorization Power of ReLU Neural Networks Gal Vardi, Gilad Yehudai, Ohad Shamir

NeurIPS 2022 Reconstructing Training Data from Trained Neural Networks Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani

COLT 2022 Width Is Less Important than Depth in ReLU Neural Networks Gal Vardi, Gilad Yehudai, Ohad Shamir

ICML 2021 From Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron

NeurIPS 2021 Learning a Single Neuron with Bias Using Gradient Descent Gal Vardi, Gilad Yehudai, Ohad Shamir

COLT 2021 The Connection Between Approximation, Depth Separation and Learnability in Neural Networks Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, Ohad Shamir

COLT 2021 The Effects of Mild Over-Parameterization on the Optimization Landscape of Shallow ReLU Neural Networks Itay M Safran, Gilad Yehudai, Ohad Shamir

COLT 2020 Learning a Single Neuron with Gradient Methods Gilad Yehudai, Shamir Ohad

ICML 2020 Proving the Lottery Ticket Hypothesis: Pruning Is All You Need Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, Ohad Shamir

NeurIPS 2019 On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai, Ohad Shamir