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..
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
Authors: Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 2: Error rates of different certifiably trained models on CIFAR-10 and Tiny-Image Net datasets (results on downscaled Image Net are in Table 4). Table 3: Per-epoch training time and memory usage of 4 large models on CIFAR-10 with batch size 256, and 3 large models on Tiny-Image Net with batch size 100. |
| Researcher Affiliation | Academia | 1Northeastern University 2Tsinghua University 3UCLA 4DCST, THUAI, SKLits, BNRist, Tsinghua University 5Lawrence Livermore National Laboratory |
| Pseudocode | Yes | Algorithm 1 Forward Mode Bound Propagation on General Computational Graphs, Algorithm 2 Backward Mode Bound Propagation on a General Computational Graph |
| Open Source Code | Yes | Our open source library is available at https://github.com/Kaidi Xu/auto_Li RPA. |
| Open Datasets | Yes | We report results on CIFAR-10 [25] with ℓ perturbation ϵ=8/255 and Tiny-Image Net with ϵ=1/255 in Table 2, and Downscaled-Image Net [5] which has 1, 000 class labels with ℓ perturbation ϵ=1/255 in Table 4. |
| Dataset Splits | No | The paper discusses training and test errors but does not explicitly provide details about validation dataset splits or how they were derived for reproducibility. |
| Hardware Specification | Yes | We use the same batch size 256 for all settings and conduct the experiments on 4 Nvidia GTX 1080Ti GPUs. |
| Software Dependencies | No | The paper mentions using “Py Torch” but does not specify version numbers for any software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | We use the same batch size 256 for all settings and conduct the experiments on 4 Nvidia GTX 1080Ti GPUs. We provide detailed hyperparameters in Appendix C.1. We consider synonym-based word substitution with δ 6 (up to 6 word substitutions). We provide more backgrounds and training details in Appendix C.2. |