Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness
Authors: Vaclav Voracek
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical validation of the proposed methods confirming the theory. ... We benchmark the confidence sequences on the Sequential decision making task, where we try to certify a certain radius at given confidence level with as few samples as possible... Cifar10, ℓ2, details are in Appendix C.2.1 |
| Researcher Affiliation | Academia | Václav Voráˇcek Tübingen AI center, University of Tübingen vaclav.voracek@uni-tuebingen.de |
| Pseudocode | Yes | Algorithm 1 Union-Bound Confidence Sequence... Algorithm 2 Betting Confidence Sequence |
| Open Source Code | Yes | The code can be found at https://github.com/vvoracek/RS_conf_seq. |
| Open Datasets | Yes | right: Comparison of ℓ2robustness curves with the standard (dashed) or the randomized (solid) Clopper-Pearson bounds on a CIFAR-10 dataset under the standard setting. The experimental details are in Appendix C. |
| Dataset Splits | No | The paper mentions using 'CIFAR-10 dataset' and refers to 'test dataset' in its experimental details (Appendix C.1) but does not explicitly state the training/validation/test dataset splits, such as percentages or specific counts for each partition, nor does it explicitly mention the use or creation of a validation set beyond the standard test set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only vaguely mentions 'single GPU' in the NeurIPS checklist. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers, such as 'PyTorch 1.9' or 'CUDA 11.1', which are needed to replicate the experiments. |
| Experiment Setup | Yes | We had Wide Res Net-40-2 for CIFAR-10 trained for 120 epochs with SGD and learning rate 0.1, Nesterov momentum 0.9, weight decay 0.0001 and cosine annealing. batch size 64. |