Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Authors: Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi
ICML 2020 | Venue PDF | 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 <EMAIL>, Soheil Feizi <EMAIL>. |
| 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). |