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Gilad Yehudai
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2020 – today
- 2024
- [c15]Amit Daniely, Mariano Schain, Gilad Yehudai:
RedEx: Beyond Fixed Representation Methods via Convex Optimization. ALT 2024: 518-543 - [i21]Amit Daniely, Mariano Schain, Gilad Yehudai:
RedEx: Beyond Fixed Representation Methods via Convex Optimization. CoRR abs/2401.07606 (2024) - [i20]Odelia Melamed, Gilad Yehudai, Adi Shamir:
MALT Powers Up Adversarial Attacks. CoRR abs/2407.02240 (2024) - [i19]Gilad Yehudai, Haim Kaplan, Asma Ghandeharioun, Mor Geva, Amir Globerson:
When Can Transformers Count to n? CoRR abs/2407.15160 (2024) - [i18]Yakir Oz, Gilad Yehudai, Gal Vardi, Itai Antebi, Michal Irani, Niv Haim:
Reconstructing Training Data From Real World Models Trained with Transfer Learning. CoRR abs/2407.15845 (2024) - [i17]Noah Amsel, Gilad Yehudai, Joan Bruna:
On the Benefits of Rank in Attention Layers. CoRR abs/2407.16153 (2024) - 2023
- [c14]Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Yakir Oz, Yaniv Nikankin, Michal Irani:
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses. NeurIPS 2023 - [c13]Guy Kornowski, Gilad Yehudai, Ohad Shamir:
From Tempered to Benign Overfitting in ReLU Neural Networks. NeurIPS 2023 - [c12]Odelia Melamed, Gilad Yehudai, Gal Vardi:
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces. NeurIPS 2023 - [i16]Odelia Melamed, Gilad Yehudai, Gal Vardi:
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds. CoRR abs/2303.00783 (2023) - [i15]Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Michal Irani:
Reconstructing Training Data from Multiclass Neural Networks. CoRR abs/2305.03350 (2023) - [i14]Guy Kornowski, Gilad Yehudai, Ohad Shamir:
From Tempered to Benign Overfitting in ReLU Neural Networks. CoRR abs/2305.15141 (2023) - [i13]Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Yakir Oz, Yaniv Nikankin, Michal Irani:
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses. CoRR abs/2307.01827 (2023) - [i12]Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Locally Optimal Descent for Dynamic Stepsize Scheduling. CoRR abs/2311.13877 (2023) - 2022
- [c11]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. COLT 2022: 1249-1281 - [c10]Gal Vardi, Gilad Yehudai, Ohad Shamir:
On the Optimal Memorization Power of ReLU Neural Networks. ICLR 2022 - [c9]Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data From Trained Neural Networks. NeurIPS 2022 - [c8]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. NeurIPS 2022 - [i11]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. CoRR abs/2202.03841 (2022) - [i10]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. CoRR abs/2202.04347 (2022) - [i9]Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data from Trained Neural Networks. CoRR abs/2206.07758 (2022) - 2021
- [c7]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. COLT 2021: 3265-3295 - [c6]Itay Safran, Gilad Yehudai, Ohad Shamir:
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. COLT 2021: 3889-3934 - [c5]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
From Local Structures to Size Generalization in Graph Neural Networks. ICML 2021: 11975-11986 - [c4]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Bias Using Gradient Descent. NeurIPS 2021: 28690-28700 - [i8]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. CoRR abs/2102.00434 (2021) - [i7]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Bias Using Gradient Descent. CoRR abs/2106.01101 (2021) - [i6]Gal Vardi, Gilad Yehudai, Ohad Shamir:
On the Optimal Memorization Power of ReLU Neural Networks. CoRR abs/2110.03187 (2021) - 2020
- [c3]Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Gradient Methods. COLT 2020: 3756-3786 - [c2]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. ICML 2020: 6682-6691 - [i5]Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Gradient Methods. CoRR abs/2001.05205 (2020) - [i4]Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir:
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. CoRR abs/2002.00585 (2020) - [i3]Itay Safran, Gilad Yehudai, Ohad Shamir:
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. CoRR abs/2006.01005 (2020) - [i2]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
On Size Generalization in Graph Neural Networks. CoRR abs/2010.08853 (2020)
2010 – 2019
- 2019
- [c1]Gilad Yehudai, Ohad Shamir:
On the Power and Limitations of Random Features for Understanding Neural Networks. NeurIPS 2019: 6594-6604 - [i1]Gilad Yehudai, Ohad Shamir:
On the Power and Limitations of Random Features for Understanding Neural Networks. CoRR abs/1904.00687 (2019)
Coauthor Index
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last updated on 2024-08-20 20:29 CEST by the dblp team
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