Gilad Yehudai
20 papers · 2019–2026 · 7 venues · across top CS/AI venues
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Optimization & Theory Β· Machine Learning
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Optimization & Theory Β· Deep Learning
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Keywords
neural network
(6)
relu network
(5)
gradient descent
(4)
adversarial example
(3)
single neuron
(2)
depth separation
(2)
adversarial robustness
(2)
implicit bia
(2)
data reconstruction
(2)
representation learning
(2)
relu neural network
(2)
graph classification
(1)
privacy attack
(1)
self-supervised learning
(1)
function approximation
(1)
convex optimization
(1)
lottery ticket hypothesis
(1)
neural network pruning
(1)
semidefinite programming
(1)
non-convex optimization
(1)
Papers
When Can Transformers Count to n?
UAI 2026
Locally Optimal Descent for Dynamic Stepsize Scheduling
AISTATS 2025
Logarithmic Width Suffices for Robust Memorization
COLT 2025
Quality over Quantity in Attention Layers: When Adding More Heads Hurts
ICLR 2025
MALT Powers Up Adversarial Attacks
NeurIPS 2024
RedEx: Beyond Fixed Representation Methods via Convex Optimization
ALT 2024
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
NeurIPS 2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
NeurIPS 2023
From Tempered to Benign Overfitting in ReLU Neural Networks
NeurIPS 2023
Gradient Methods Provably Converge to Non-Robust Networks
NeurIPS 2022
On the Optimal Memorization Power of ReLU Neural Networks
ICLR 2022
Reconstructing Training Data From Trained Neural Networks
NeurIPS 2022
Width is Less Important than Depth in ReLU Neural Networks
COLT 2022
From Local Structures to Size Generalization in Graph Neural Networks
ICML 2021
Learning a Single Neuron with Bias Using Gradient Descent
NeurIPS 2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks
COLT 2021
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks
COLT 2021
Learning a Single Neuron with Gradient Methods
COLT 2020
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
ICML 2020
On the Power and Limitations of Random Features for Understanding Neural Networks
NeurIPS 2019