Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Authors: Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results on CIFAR to validate our theory. For other smoothing distributions, such as, a uniform distribution within an ℓ1 or an ℓ∞-norm ball, we show upper bounds of the form O(1/d) and O(1/d1−1/p) respectively, which have an even worse dependence on d. |
| Researcher Affiliation | Academia | 1University of Maryland, College Park, Maryland, USA. Correspondence to: Aounon Kumar <aounon@umd.edu>, Soheil Feizi <sfeizi@cs.umd.edu>. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for our experiments is available on Git Hub at: https://github.com/alevine0/ smoothing Gen Gaussian |
| Open Datasets | Yes | We present experimental results on CIFAR to validate our theory. We provide empirical evidence to support our claims on the CIFAR-10 dataset. |
| Dataset Splits | No | The paper mentions using 100,000 samples for estimating p1(x) and p2(x), but does not explicitly state train/validation/test dataset splits (e.g., percentages or counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud instance specifications). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python version, library versions like PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | We specifically tested on CIFAR-10 (32x32 pixels), as well as scaled-down versions of this dataset (16x16 and 8x8 pixels)... Note that we re-trained the classifier on noisy images for each noise distribution and standard deviation σ. For a fixed standard deviation σ... (Figure 5) ... (σ = .12) ... (σ = .25). |