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2020 – today
- 2024
[c12]Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry:
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. ICML 2024- [i10]Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry:
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. CoRR abs/2402.06323 (2024) - 2022
- [b1]Alon Brutzkus:
Towards Understanding Optimization and Generalization in Deep Learning. Tel Aviv University, Israel, 2022 - [c11]Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz:
Efficient Learning of CNNs using Patch Based Features. ICML 2022: 2336-2356 - [c10]Ido Bronstein, Alon Brutzkus, Amir Globerson:
On the inductive bias of neural networks for learning read-once DNFs. UAI 2022: 255-265 - 2021
- [c9]Alon Brutzkus, Pinchas Akiva, Guy Amit:
To Deep or Not to Deep: Comparison of Traditional and Deep Learning Models in Disease Prediction from Electronic Health Records. AMIA 2021 - [c8]Roei Sarussi, Alon Brutzkus, Amir Globerson:
Towards Understanding Learning in Neural Networks with Linear Teachers. ICML 2021: 9313-9322 - [c7]Gal Shachaf, Alon Brutzkus, Amir Globerson:
A Theoretical Analysis of Fine-tuning with Linear Teachers. NeurIPS 2021: 15382-15394 - [c6]Alon Brutzkus, Amir Globerson:
An optimization and generalization analysis for max-pooling networks. UAI 2021: 1650-1660 - [i9]Roei Sarussi, Alon Brutzkus, Amir Globerson:
Towards Understanding Learning in Neural Networks with Linear Teachers. CoRR abs/2101.02533 (2021) - [i8]Gal Shachaf, Alon Brutzkus, Amir Globerson:
A Theoretical Analysis of Fine-tuning with Linear Teachers. CoRR abs/2107.01641 (2021) - 2020
- [c5]Alon Brutzkus, Amit Daniely, Eran Malach:
ID3 Learns Juntas for Smoothed Product Distributions. COLT 2020: 902-915 - [i7]Alon Brutzkus, Amir Globerson:
On the Inductive Bias of a CNN for Orthogonal Patterns Distributions. CoRR abs/2002.09781 (2020)
2010 – 2019
- 2019
- [c4]Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha:
Low Latency Privacy Preserving Inference. ICML 2019: 812-821 - [c3]Alon Brutzkus, Amir Globerson:
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. ICML 2019: 822-830 - [i6]Alon Brutzkus, Amit Daniely, Eran Malach:
ID3 Learns Juntas for Smoothed Product Distributions. CoRR abs/1906.08654 (2019) - [i5]Alon Brutzkus, Amit Daniely, Eran Malach:
On the Optimality of Trees Generated by ID3. CoRR abs/1907.05444 (2019) - 2018
- [c2]Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz:
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. ICLR (Poster) 2018 - [i4]Alon Brutzkus, Amir Globerson:
Over-parameterization Improves Generalization in the XOR Detection Problem. CoRR abs/1810.03037 (2018) - [i3]Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach:
Low Latency Privacy Preserving Inference. CoRR abs/1812.10659 (2018) - 2017
- [c1]Alon Brutzkus, Amir Globerson:
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. ICML 2017: 605-614 - [i2]Alon Brutzkus, Amir Globerson:
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. CoRR abs/1702.07966 (2017) - [i1]Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz:
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. CoRR abs/1710.10174 (2017) - 2015
- [j1]Gilad Braunschvig, Alon Brutzkus, David Peleg, Adam Sealfon:
Truth tellers and liars with fewer questions. Discret. Math. 338(8): 1310-1316 (2015)
Coauthor Index
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