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..
Certified Adversarial Robustness Under the Bounded Support Set
Authors: Yiwen Kou, Qinyuan Zheng, Yisen Wang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results on CIFAR-10 dataset with Res Net model to validate part of our theory about uniform smoothing measures with l2 ball and l ball support set on l2 adversary and use Gaussian smoothing measure as contrast. |
| Researcher Affiliation | Academia | 1Yuanpei College, Peking University 2Key Lab. of Machine Perception (Mo E), School of Artificial Intelligence, Peking University. 3Institute for Artificial Intelligence, Peking University. |
| Pseudocode | Yes | Algorithm 1 Certification Process |
| Open Source Code | No | The paper mentions using an implementation from GitHub for comparison purposes but does not state that their own methodology's code is open-source or provided. |
| Open Datasets | Yes | We choose CIFAR-10 as our main dataset and Res Net-110 as our base classifier. |
| Dataset Splits | No | The paper states: 'We first train the base classifier on the 50000 image training set without smoothing and achieve 89.6% prediction accuracy on the 10000 image test set.' It specifies training and test sets but does not explicitly mention a separate validation set or its split details. |
| Hardware Specification | Yes | All training, testing, and certification are run on an NVIDIA RTX 3090. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the sample amount n to 100, 1000, and 10000 with three different smoothing distributions, and they all obtain similar results: it takes only 10 minutes to run through the 10000 images test set with 100 samples for each image, 30 minutes with 1000 samples and 3 hours with excessive 10000 samples. We first implement our framework with Gaussian smoothing measure N(x, σ2I) where σ = 0.025, 0.05, 0.1 and sample amount n=100. Next, for smoothing process, we substitute Gaussian distribution with l2, l norm ball support set uniform distribution, with r = 0.025, 0.05, 0.1. |