Gal Vardi

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 66

Top Topics

Number Of Hidden Neurons

Inductive Learning Algorithms

Top Venues

Publications

Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. ICLR (2024)
Yakir Oz, Gilad Yehudai, Gal Vardi, Itai Antebi, Michal Irani, Niv Haim
Reconstructing Training Data From Real World Models Trained with Transfer Learning. CoRR (2024)
Orna Kupferman, Gal Vardi
Perspective Games. ACM Trans. Comput. Log. 25 (1) (2024)
Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi, Wei Hu
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data. ICLR (2024)
Nirmit Joshi, Gal Vardi, Nathan Srebro
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. ICLR (2024)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR (2023)
Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi, Wei Hu
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data. CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. COLT (2023)
Amit Daniely, Nathan Srebro, Gal Vardi
Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR (2023)
Nadav Timor, Gal Vardi, Ohad Shamir
Implicit Regularization Towards Rank Minimization in ReLU Networks. ALT (2023)
Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. CoRR (2023)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS (2023)
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)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. CoRR (2023)
Amit Daniely, Nati Srebro, Gal Vardi
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS (2023)
Gal Vardi
On the Implicit Bias in Deep-Learning Algorithms. Commun. ACM 66 (6) (2023)
Nirmit Joshi, Gal Vardi, Nathan Srebro
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. CoRR (2023)
Odelia Melamed, Gilad Yehudai, Gal Vardi
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces. NeurIPS (2023)
Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Michal Irani
Reconstructing Training Data from Multiclass Neural Networks. CoRR (2023)
Odelia Melamed, Gilad Yehudai, Gal Vardi
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds. CoRR (2023)
Amit Daniely, Nati Srebro, Gal Vardi
Most Neural Networks Are Almost Learnable. NeurIPS (2023)
Amit Daniely, Nathan Srebro, Gal Vardi
Most Neural Networks Are Almost Learnable. CoRR (2023)
Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Yakir Oz, Yaniv Nikankin, Michal Irani
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses. CoRR (2023)
Gal Vardi, Gilad Yehudai, Ohad Shamir
On the Optimal Memorization Power of ReLU Neural Networks. ICLR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Gradient Methods Provably Converge to Non-Robust Networks. NeurIPS (2022)
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR (2022)
Gal Vardi, Ohad Shamir, Nati Srebro
On Margin Maximization in Linear and ReLU Networks. NeurIPS (2022)
Itay Safran, Gal Vardi, Jason D. Lee
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. NeurIPS (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Gradient Methods Provably Converge to Non-Robust Networks. CoRR (2022)
Gal Vardi, Ohad Shamir, Nathan Srebro
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR (2022)
Gal Vardi
On the Implicit Bias in Deep-Learning Algorithms. CoRR (2022)
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani
Reconstructing Training Data from Trained Neural Networks. CoRR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Width is Less Important than Depth in ReLU Neural Networks. CoRR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Width is Less Important than Depth in ReLU Neural Networks. COLT (2022)
Itay Safran, Gal Vardi, Jason D. Lee
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. CoRR (2022)
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani
Reconstructing Training Data From Trained Neural Networks. NeurIPS (2022)
Nadav Timor, Gal Vardi, Ohad Shamir
Implicit Regularization Towards Rank Minimization in ReLU Networks. CoRR (2022)
Gal Vardi, Ohad Shamir, Nati Srebro
The Sample Complexity of One-Hidden-Layer Neural Networks. NeurIPS (2022)
Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir
Size and Depth Separation in Approximating Benign Functions with Neural Networks. COLT (2021)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Learning a Single Neuron with Bias Using Gradient Descent. CoRR (2021)
Amit Daniely, Gal Vardi
From Local Pseudorandom Generators to Hardness of Learning. COLT (2021)
Gal Vardi, Ohad Shamir
Implicit Regularization in ReLU Networks with the Square Loss. COLT (2021)
Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir
Size and Depth Separation in Approximating Natural Functions with Neural Networks. CoRR (2021)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Learning a Single Neuron with Bias Using Gradient Descent. NeurIPS (2021)
Amit Daniely, Gal Vardi
From Local Pseudorandom Generators to Hardness of Learning. CoRR (2021)
Gal Vardi, Gilad Yehudai, Ohad Shamir
On the Optimal Memorization Power of ReLU Neural Networks. CoRR (2021)
Gal Vardi, Ohad Shamir, Nathan Srebro
On Margin Maximization in Linear and ReLU Networks. CoRR (2021)
Amit Daniely, Gal Vardi
Hardness of Learning Neural Networks with Natural Weights. CoRR (2020)
Gal Vardi, Ohad Shamir
Neural Networks with Small Weights and Depth-Separation Barriers. Electron. Colloquium Comput. Complex. 27 (2020)
Gal Vardi, Ohad Shamir
Neural Networks with Small Weights and Depth-Separation Barriers. NeurIPS (2020)
Gal Vardi, Ohad Shamir
Neural Networks with Small Weights and Depth-Separation Barriers. CoRR (2020)
Gal Vardi, Ohad Shamir
Implicit Regularization in ReLU Networks with the Square Loss. CoRR (2020)
Amit Daniely, Gal Vardi
Hardness of Learning Neural Networks with Natural Weights. NeurIPS (2020)
Shibashis Guha, Orna Kupferman, Gal Vardi
Multi-player flow games. Auton. Agents Multi Agent Syst. 33 (6) (2019)
Orna Kupferman, Gal Vardi
Perspective Games. LICS (2019)
Orna Kupferman, Gal Vardi
Flow Logic. Log. Methods Comput. Sci. 15 (4) (2019)
Dan Hefetz, Orna Kupferman, Amir Lellouche, Gal Vardi
Spanning-Tree Games. MFCS (2018)
Orna Kupferman, Gal Vardi
The Unfortunate-Flow Problem. ICALP (2018)
Shibashis Guha, Orna Kupferman, Gal Vardi
Multi-Player Flow Games. AAMAS (2018)
Orna Kupferman, Gal Vardi
Flow Logic. CoRR (2018)
Orna Kupferman, Gal Vardi
On relative and probabilistic finite counterability. Formal Methods Syst. Des. 52 (2) (2018)
Orna Kupferman, Gal Vardi
Flow Logic. CONCUR (2017)
Orna Kupferman, Gal Vardi, Moshe Y. Vardi
Flow Games. FSTTCS (2017)
Orna Kupferman, Gal Vardi
Eulerian Paths with Regular Constraints. MFCS (2016)
Orna Kupferman, Gal Vardi
On Relative and Probabilistic Finite Counterability. CSL (2015)