Adversarial Robustness is at Odds with Lazy Training
Authors: Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we provide empirical support for our theory, and conclude with a discussion in Section 6. 5 Experiments The primary goal of this section is to provide empirical support for our theoretical findings. |
| Researcher Affiliation | Academia | Yunjuan Wang Department of Computer Science Johns Hopkins University Baltimore, MD, 21218 ywang509@jhu.edu |
| Pseudocode | Yes | To ensure that the updates remain in the lazy regime, after each gradient step, we project the weights onto B2, (W0, V ); see Algo. 1 in the appendix for details. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See supplemental material. |
| Open Datasets | Yes | We utilize the MNIST dataset for our empirical study. MNIST is a dataset of 28 28 greyscale handwritten digits [Le Cun et al., 1998]. |
| Dataset Splits | Yes | We extract examples corresponding to images of the digits 0 and 1 , resulting in 12665 training examples and 2115 test examples. |
| Hardware Specification | No | The paper does not specify any particular hardware, such as GPU or CPU models, or cloud computing resources used for the experiments. The checklist explicitly states 'N/A' for 'total amount of compute and the type of resources used'. |
| Software Dependencies | No | The paper describes the algorithms used (standard SGD, PGD) but does not specify any software libraries or dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | We train the network using standard SGD on the logistic loss using a learning rate of 0.1. ... We experiment with C0 {10, 20, 30, 40}. ... For this set of experiments, we also normalize the data, so that x = 1 ... We experiment with values of V and the size of δ in the intervals 0.05 V 1 and 0.05 δ 1, respectively. |