| 2024 | ICML | How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. | Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry |
| 2022 | ICML | Efficient Learning of CNNs using Patch Based Features. | Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz |
| 2022 | UAI | On the inductive bias of neural networks for learning read-once DNFs. | Ido Bronstein, Alon Brutzkus, Amir Globerson |
| 2021 | AMIA | To Deep or Not to Deep: Comparison of Traditional and Deep Learning Models in Disease Prediction from Electronic Health Records. | Alon Brutzkus, Pinchas Akiva, Guy Amit |
| 2021 | ICML | Towards Understanding Learning in Neural Networks with Linear Teachers. | Roei Sarussi, Alon Brutzkus, Amir Globerson |
| 2021 | UAI | An optimization and generalization analysis for max-pooling networks. | Alon Brutzkus, Amir Globerson |
| 2020 | COLT | ID3 Learns Juntas for Smoothed Product Distributions. | Alon Brutzkus, Amit Daniely, Eran Malach |
| 2019 | ICML | Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. | Alon Brutzkus, Amir Globerson |
| 2019 | ICML | Low Latency Privacy Preserving Inference. | Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha |
| 2018 | ICLR | SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. | Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz |
| 2017 | ICML | Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. | Alon Brutzkus, Amir Globerson |