On the Power and Limitations of Random Features for Understanding Neural Networks. - Gilad Yehudai; Ohad Shamir (NeurIPS, 2019)

Learning a Single Neuron with Gradient Methods. - Gilad Yehudai; Ohad Shamir (COLT, 2020)

Proving the Lottery Ticket Hypothesis: Pruning is All You Need. - Eran Malach; Gilad Yehudai; Shai Shalev-Shwartz; Ohad Shamir (ICML, 2020)

On Size Generalization in Graph Neural Networks. - Gilad Yehudai; Ethan Fetaya; Eli A. Meirom; Gal Chechik; Haggai Maron (CoRR, 2020)

The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. - Eran Malach; Gilad Yehudai; Shai Shalev-Shwartz; Ohad Shamir (COLT, 2021)

The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. - Itay Safran; Gilad Yehudai; Ohad Shamir (COLT, 2021)

From Local Structures to Size Generalization in Graph Neural Networks. - Gilad Yehudai; Ethan Fetaya; Eli A. Meirom; Gal Chechik; Haggai Maron (ICML, 2021)

Learning a Single Neuron with Bias Using Gradient Descent. - Gal Vardi; Gilad Yehudai; Ohad Shamir (NeurIPS, 2021)

Width is Less Important than Depth in ReLU Neural Networks. - Gal Vardi; Gilad Yehudai; Ohad Shamir (COLT, 2022)

On the Optimal Memorization Power of ReLU Neural Networks. - Gal Vardi; Gilad Yehudai; Ohad Shamir (ICLR, 2022)

Reconstructing Training Data From Trained Neural Networks. - Niv Haim; Gal Vardi; Gilad Yehudai; Ohad Shamir; Michal Irani (NeurIPS, 2022)

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

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 (NeurIPS, 2023)

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

Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds. - Odelia Melamed; Gilad Yehudai; Gal Vardi (CoRR, 2023)

Reconstructing Training Data from Multiclass Neural Networks. - Gon Buzaglo; Niv Haim; Gilad Yehudai; Gal Vardi; Michal Irani (CoRR, 2023)

RedEx: Beyond Fixed Representation Methods via Convex Optimization. - Amit Daniely; Mariano Schain; Gilad Yehudai (ALT, 2024)

MALT Powers Up Adversarial Attacks. - Odelia Melamed; Gilad Yehudai; Adi Shamir (NeurIPS, 2024)

When Can Transformers Count to n? - Gilad Yehudai; Haim Kaplan; Asma Ghandeharioun; Mor Geva; Amir Globerson (CoRR, 2024)

Reconstructing Training Data From Real World Models Trained with Transfer Learning. - Yakir Oz; Gilad Yehudai; Gal Vardi; Itai Antebi; Michal Irani; Niv Haim (CoRR, 2024)

On the Benefits of Rank in Attention Layers. - Noah Amsel; Gilad Yehudai; Joan Bruna (CoRR, 2024)

On the Reconstruction of Training Data from Group Invariant Networks. - Ran Elbaz; Gilad Yehudai; Meirav Galun; Haggai Maron (CoRR, 2024)

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

Logarithmic Width Suffices for Robust Memorization. - Amitsour Egosi; Gilad Yehudai; Ohad Shamir (COLT, 2025)

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

Compositional Reasoning with Transformers, RNNs, and Chain of Thought. - Gilad Yehudai; Noah Amsel; Joan Bruna (CoRR, 2025)

Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers. - Gilad Yehudai; Clayton Sanford; Maya Bechler-Speicher; Orr Fischer; Ran Gilad-Bachrach; Amir Globerson (CoRR, 2025)

Querying Kernel Methods Suffices for Reconstructing their Training Data. - Daniel Barzilai; Yuval Margalit; Eitan Gronich; Gilad Yehudai; Meirav Galun; Ronen Basri (CoRR, 2025)