Certifying Confidence via Randomized Smoothing
Authors: Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 aounon@umd.edu Alexander Levine University of Maryland alevine0@cs.umd.edu Soheil Feizi University of Maryland sfeizi@cs.umd.edu Tom Goldstein University of Maryland tomg@cs.umd.edu |
| 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. |