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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Invariance-Aware Randomized Smoothing Certificates
Authors: Jan Schuchardt, Stephan Günnemann
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 24,129 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,093 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate that the provably tight certificates can offer much stronger guarantees, but that in practical scenarios the orbit-based method is a good approximation. |
| Researcher Affiliation | Academia | Jan Schuchardt Technical University of Munich EMAIL Stephan Günnemann Technical University of Munich EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | A reference implementation will be made available at https://www.cs.cit.tum.de/daml/invariance-smoothing. |
| Open Datasets | Yes | We consider two datasets: 3D point cloud representations of Model Net40 [126], which consists of CAD models from 40 different categories, and 2D point cloud representations of MNIST [127]. |
| Dataset Splits | No | The paper mentions 'default test sets' but does not specify exact percentages, sample counts, or a detailed methodology for training/validation splits. |
| Hardware Specification | Yes | one can use a large number of samples to obtain narrow bounds at little computational cost (e.g. 0.59s for 100000 samples per confidence bound on an Intel Xeon E5-2630 CPU). |
| Software Dependencies | No | The paper does not list specific software components with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | All parameters and experimental details are specified in Appendix B. We use 10000 samples per confidence bound and set α = 0.001, i.e. all certificates hold with 99.9% probability. |