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..
Certifying Confidence via Randomized Smoothing
Authors: Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on CIFAR-10 and Image Net datasets show that using information about the distribution of the confidence scores allows us to achieve a significantly better certified radius than ignoring it. |
| Researcher Affiliation | Academia | Aounon Kumar University of Maryland EMAIL Alexander Levine University of Maryland EMAIL Soheil Feizi University of Maryland EMAIL Tom Goldstein University of Maryland EMAIL |
| Pseudocode | No | The paper describes its methods in prose and does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for the experiments is available at https://github.com/aounon/cdf-smoothing. |
| Open Datasets | Yes | Our experimental results on CIFAR-10 and Image Net datasets show that using information about the distribution of the confidence scores allows us to achieve a significantly better certified radius than ignoring it. |
| Dataset Splits | No | The paper mentions using ResNet models trained by Cohen et al. in [7] on CIFAR-10 and Image Net datasets, but it does not explicitly state the training, validation, or test dataset splits used for its own experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies (e.g., libraries, frameworks) used in the experiments. |
| Experiment Setup | Yes | We use the same number of samples m = 100, 000 and value of α = 0.001 as in [7]. We set s1, s2. . . . , sn in theorem 2 such that the number of confidence score values falling in each of the intervals (a, s1), (s1, s2), . . . , (sn, b) is the same. We use the same σ for certifying confidences as well. |