Adversarially Robust Conformal Prediction
Authors: Asaf Gendler, Tsui-Wei Weng, Luca Daniel, Yaniv Romano
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our proposed methods on three benchmark image classification data sets: CIFAR10, CIFAR100 (Krizhevsky, 2009), and Image Net ILSVRC2012 (Deng et al., 2009), which are described in Supplementary Section S2. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Technion Israel Institute of Technology 2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology |
| Pseudocode | Yes | Algorithm 1 RSCP: Randomly Smoothed Conformal Prediction |
| Open Source Code | Yes | A Python package that implements our methods and code to reproduce our experiments are available at https://github.com/Asafgendler/RSCP. |
| Open Datasets | Yes | We evaluate the performance of our proposed methods on three benchmark image classification data sets: CIFAR10, CIFAR100 (Krizhevsky, 2009), and Image Net ILSVRC2012 (Deng et al., 2009), which are described in Supplementary Section S2. |
| Dataset Splits | Yes | Then, we split the remaining data into two equally sized disjoint subsets, one is used for calibration and the second for testing. |
| Hardware Specification | Yes | We train the models using Pytorch, on a single Nvidia GEFORCE GTX 1080 Ti GPU. |
| Software Dependencies | No | We train the models using Pytorch, on a single Nvidia GEFORCE GTX 1080 Ti GPU. This mentions Pytorch but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | For all data sets, we choose Res Net (He et al., 2016) to be the base architecture of the deep net classifier and fit two different models for each data set: one on clean training points and the second on points that are augmented with Gaussian noise of standard deviation σ that is equal to the smoothing parameter from (10). |