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ORCIDGal Vardi
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2020 – today
- 2024
[j5]Orna Kupferman, Gal Vardi:
Perspective Games. ACM Trans. Comput. Log. 25(1): 4:1-4:26 (2024)- [c32]Nirmit Joshi, Gal Vardi, Nathan Srebro:
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. ICLR 2024 - [c31]Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi, Wei Hu:
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data. ICLR 2024 - [c30]Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro:
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. ICLR 2024 - [i32]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) - [i31]David Yunis, Kumar Kshitij Patel, Samuel Wheeler, Pedro Savarese, Gal Vardi, Karen Livescu, Michael Maire, Matthew R. Walter:
Approaching Deep Learning through the Spectral Dynamics of Weights. CoRR abs/2408.11804 (2024) - [i30]Marko Medvedev, Gal Vardi, Nathan Srebro:
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality. CoRR abs/2409.03891 (2024) - [i29]Spencer Frei, Gal Vardi:
Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context. CoRR abs/2410.01774 (2024) - 2023
- [j4]Gal Vardi:
On the Implicit Bias in Deep-Learning Algorithms. Commun. ACM 66(6): 86-93 (2023) - [c29]Nadav Timor, Gal Vardi, Ohad Shamir:
Implicit Regularization Towards Rank Minimization in ReLU Networks. ALT 2023: 1429-1459 - [c28]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: 3173-3228 - [c27]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR 2023 - [c26]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 - [c25]Amit Daniely, Nati Srebro, Gal Vardi:
Most Neural Networks Are Almost Learnable. NeurIPS 2023 - [c24]Amit Daniely, Nati Srebro, Gal Vardi:
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy. NeurIPS 2023 - [c23]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nati Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. NeurIPS 2023 - [c22]Odelia Melamed, Gilad Yehudai, Gal Vardi:
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces. NeurIPS 2023 - [i28]Amit Daniely, Nathan Srebro, Gal Vardi:
Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR abs/2302.07426 (2023) - [i27]Odelia Melamed, Gilad Yehudai, Gal Vardi:
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds. CoRR abs/2303.00783 (2023) - [i26]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR abs/2303.01456 (2023) - [i25]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 abs/2303.01462 (2023) - [i24]Gon Buzaglo, Niv Haim, Gilad Yehudai, Gal Vardi, Michal Irani:
Reconstructing Training Data from Multiclass Neural Networks. CoRR abs/2305.03350 (2023) - [i23]Amit Daniely, Nathan Srebro, Gal Vardi:
Most Neural Networks Are Almost Learnable. CoRR abs/2305.16508 (2023) - [i22]Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro:
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. CoRR abs/2306.13185 (2023) - [i21]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) - [i20]Nirmit Joshi, Gal Vardi, Nathan Srebro:
Noisy Interpolation Learning with Shallow Univariate ReLU Networks. CoRR abs/2307.15396 (2023) - [i19]Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi, Wei Hu:
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data. CoRR abs/2310.02541 (2023) - 2022
- [c21]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. COLT 2022: 1249-1281 - [c20]Gal Vardi, Gilad Yehudai, Ohad Shamir:
On the Optimal Memorization Power of ReLU Neural Networks. ICLR 2022 - [c19]Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data From Trained Neural Networks. NeurIPS 2022 - [c18]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 - [c17]Gal Vardi, Ohad Shamir, Nati Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. NeurIPS 2022 - [c16]Gal Vardi, Ohad Shamir, Nati Srebro:
On Margin Maximization in Linear and ReLU Networks. NeurIPS 2022 - [c15]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. NeurIPS 2022 - [i18]Nadav Timor, Gal Vardi, Ohad Shamir:
Implicit Regularization Towards Rank Minimization in ReLU Networks. CoRR abs/2201.12760 (2022) - [i17]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. CoRR abs/2202.03841 (2022) - [i16]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Gradient Methods Provably Converge to Non-Robust Networks. CoRR abs/2202.04347 (2022) - [i15]Gal Vardi, Ohad Shamir, Nathan Srebro:
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR abs/2202.06233 (2022) - [i14]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 abs/2205.09072 (2022) - [i13]Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani:
Reconstructing Training Data from Trained Neural Networks. CoRR abs/2206.07758 (2022) - [i12]Gal Vardi:
On the Implicit Bias in Deep-Learning Algorithms. CoRR abs/2208.12591 (2022) - [i11]Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu:
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR abs/2210.07082 (2022) - 2021
- [c14]Amit Daniely, Gal Vardi:
From Local Pseudorandom Generators to Hardness of Learning. COLT 2021: 1358-1394 - [c13]Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir:
Size and Depth Separation in Approximating Benign Functions with Neural Networks. COLT 2021: 4195-4223 - [c12]Gal Vardi, Ohad Shamir:
Implicit Regularization in ReLU Networks with the Square Loss. COLT 2021: 4224-4258 - [c11]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Bias Using Gradient Descent. NeurIPS 2021: 28690-28700 - [i10]Amit Daniely, Gal Vardi:
From Local Pseudorandom Generators to Hardness of Learning. CoRR abs/2101.08303 (2021) - [i9]Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir:
Size and Depth Separation in Approximating Natural Functions with Neural Networks. CoRR abs/2102.00314 (2021) - [i8]Gal Vardi, Gilad Yehudai, Ohad Shamir:
Learning a Single Neuron with Bias Using Gradient Descent. CoRR abs/2106.01101 (2021) - [i7]Gal Vardi, Ohad Shamir, Nathan Srebro:
On Margin Maximization in Linear and ReLU Networks. CoRR abs/2110.02732 (2021) - [i6]Gal Vardi, Gilad Yehudai, Ohad Shamir:
On the Optimal Memorization Power of ReLU Neural Networks. CoRR abs/2110.03187 (2021) - 2020
- [c10]Amit Daniely, Gal Vardi:
Hardness of Learning Neural Networks with Natural Weights. NeurIPS 2020 - [c9]Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. NeurIPS 2020 - [i5]Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. CoRR abs/2006.00625 (2020) - [i4]Amit Daniely, Gal Vardi:
Hardness of Learning Neural Networks with Natural Weights. CoRR abs/2006.03177 (2020) - [i3]Gal Vardi, Ohad Shamir:
Implicit Regularization in ReLU Networks with the Square Loss. CoRR abs/2012.05156 (2020) - [i2]Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. Electron. Colloquium Comput. Complex. TR20 (2020)
2010 – 2019
- 2019
- [b1]Gal Vardi:
Graph-Theoretic Problems from the Viewpoint of Formal Methods (כותר נוסף בעברית: בעיות בתורת הגרפים מנקודת המבט של אימות פורמלי). Hebrew University of Jerusalem, Israel, 2019 - [j3]Shibashis Guha, Orna Kupferman, Gal Vardi
:
Multi-player flow games. Auton. Agents Multi Agent Syst. 33(6): 798-820 (2019) - [j2]Orna Kupferman, Gal Vardi:
Flow Logic. Log. Methods Comput. Sci. 15(4) (2019) - [c8]Orna Kupferman, Gal Vardi:
Perspective Games. LICS 2019: 1-13 - 2018
- [j1]Orna Kupferman, Gal Vardi:
On relative and probabilistic finite counterability. Formal Methods Syst. Des. 52(2): 117-146 (2018) - [c7]Shibashis Guha, Orna Kupferman, Gal Vardi:
Multi-Player Flow Games. AAMAS 2018: 104-112 - [c6]Orna Kupferman, Gal Vardi:
The Unfortunate-Flow Problem. ICALP 2018: 157:1-157:14 - [c5]Dan Hefetz, Orna Kupferman, Amir Lellouche, Gal Vardi:
Spanning-Tree Games. MFCS 2018: 35:1-35:16 - [i1]Orna Kupferman, Gal Vardi:
Flow Logic. CoRR abs/1806.05956 (2018) - 2017
- [c4]Orna Kupferman, Gal Vardi:
Flow Logic. CONCUR 2017: 9:1-9:18 - [c3]Orna Kupferman, Gal Vardi, Moshe Y. Vardi:
Flow Games. FSTTCS 2017: 38:38-38:16 - 2016
- [c2]Orna Kupferman, Gal Vardi:
Eulerian Paths with Regular Constraints. MFCS 2016: 62:1-62:15 - 2015
- [c1]Orna Kupferman, Gal Vardi:
On Relative and Probabilistic Finite Counterability. CSL 2015: 175-192
Coauthor Index
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last updated on 2024-11-11 22:28 CET by the dblp team
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