Invariance-Aware Randomized Smoothing Certificates
Authors: Jan Schuchardt, Stephan Günnemann
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 j.schuchardt@tum.de Stephan Günnemann Technical University of Munich s.guennemann@tum.de |
| 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. |