Gal Vardi
30 papers · 2020–2026 · 4 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (5) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (4) 🐝 Cross-Pollinator (13)
🌍
Conference Polyglot
(4)
🏃
Academic Marathon
(5)
🤝
Dynamic Duo
(11)
🔬
Deep Specialist
(15)
🏆
Keyword Champion
(3)
💎
Century Club
(29)
⚡
Prolific Year
(8)
🔥
Unstoppable
(6)
🗃️
Keyword Collector
(75)
Conferences
NIPS (15)
ICLR (7)
COLT (6)
ALT (2)
Top co-authors
Keywords
relu network
(12)
neural network
(10)
implicit bia
(5)
gradient flow
(5)
learning theory
(4)
gradient descent
(3)
benign overfitting
(3)
margin maximization
(3)
adversarial robustness
(3)
depth separation
(2)
kkt condition
(2)
data reconstruction
(2)
neural network optimization
(2)
hardness result
(2)
adversarial example
(2)
implicit regularization
(2)
pac learning
(2)
uniform convergence
(1)
ridge regression
(1)
multiclass classification
(1)
Papers
On the Hardness of Learning Regular Expressions
ALT 2026
Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context
ICLR 2025
Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
ICLR 2025
A Theory of Learning with Autoregressive Chain of Thought
COLT 2025
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
NIPS 2024
Provable Tempered Overfitting of Minimal Nets and Typical Nets
NIPS 2024
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression
ICLR 2024
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
ICLR 2024
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data
ICLR 2024
Implicit Regularization Towards Rank Minimization in ReLU Networks
ALT 2023
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
NIPS 2023
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
NIPS 2023
Most Neural Networks Are Almost Learnable
NIPS 2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
NIPS 2023
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
NIPS 2023
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization
COLT 2023
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
ICLR 2023
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
NIPS 2022
Reconstructing Training Data From Trained Neural Networks
NIPS 2022
Gradient Methods Provably Converge to Non-Robust Networks
NIPS 2022
Width is Less Important than Depth in ReLU Neural Networks
COLT 2022
On Margin Maximization in Linear and ReLU Networks
NIPS 2022
On the Optimal Memorization Power of ReLU Neural Networks
ICLR 2022
The Sample Complexity of One-Hidden-Layer Neural Networks
NIPS 2022
Size and Depth Separation in Approximating Benign Functions with Neural Networks
COLT 2021
Implicit Regularization in ReLU Networks with the Square Loss
COLT 2021
Learning a Single Neuron with Bias Using Gradient Descent
NIPS 2021
From Local Pseudorandom Generators to Hardness of Learning
COLT 2021
Hardness of Learning Neural Networks with Natural Weights
NIPS 2020
Neural Networks with Small Weights and Depth-Separation Barriers
NIPS 2020