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..
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Authors: Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that on tiny models, our method produces comparable bounds compared to exact methods that cannot scale to slightly larger models; on larger models, our method efficiently produces tighter results than existing relaxed or naive methods, and our method scales to much larger practical models that previous works could not handle. |
| Researcher Affiliation | Collaboration | Zhouxing Shi1, Yihan Wang1, Huan Zhang2, Zico Kolter2,3, Cho-Jui Hsieh1 1University of California, Los Angeles 2Carnegie Mellon University 3Bosch Center for AI |
| Pseudocode | No | The paper describes its methodology in text and figures but does not include structured pseudocode or algorithm blocks with explicit labels like 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code is available at https: //github.com/shizhouxing/Local-Lipschitz-Constants. |
| Open Datasets | Yes | We conduct experiments on image datasets including MNIST [30], CIFAR-10 [27], and Tiny Imagenet [29]. |
| Dataset Splits | No | The paper mentions evaluating on a 'test set' but does not explicitly provide details about a validation dataset split (e.g., percentages, sample counts, or a specific citation for validation splits). |
| Hardware Specification | No | The paper does not explicitly provide specific hardware details (e.g., exact GPU/CPU models, memory, or cloud provider specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions tools like 'PyTorch' (Appendix A.1) but does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper mentions timeout settings for baselines ('We set a timeout of 1000s for Lip MIP and Lip SDP, and 60s for Ba B.') and refers to prior work for training details ('We follow Jordan & Dimakis [24] and train several small models on a synthetic dataset.'), but it does not explicitly list specific hyperparameters such as learning rate, batch size, or optimizer settings for its own models. |