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..
Lipschitz-Certifiable Training with a Tight Outer Bound
Authors: Sungyoon Lee, Jaewook Lee, Saerom Park
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we show that BCP achieves a tighter outer bound than the global Lipschitz-based outer bound. |
| Researcher Affiliation | Academia | Sungyoon Lee Seoul National University Seoul, Korea EMAIL Jaewook Lee Seoul National University Seoul, Korea EMAIL Saerom Park Sungshin Women s University Seoul, Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 Box Constraint Propagation (BCP) Certifiable Training |
| Open Source Code | Yes | Our code is available at https://github.com/sungyoon-lee/bcp. |
| Open Datasets | Yes | We demonstrate that the proposed method can provide a tight outer bound for ℓ2-perturbation set and train certifiably robust networks, comparing its performance against state-of-the-art certifiable training methods (LMT [36], CAP [40], and IBP [13]) on MNIST and CIFAR10. Moreover, we also show that the BCP scheme can scale to Tiny Image Net and obtain a meaningful verification accuracy. |
| Dataset Splits | No | The paper mentions using MNIST, CIFAR-10, and Tiny Image Net datasets but does not explicitly provide the train/validation/test split percentages or counts within the main text. |
| Hardware Specification | Yes | We evaluate the computation times on a single Titan X GPU. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., specific libraries, frameworks, or solvers with their versions). |
| Experiment Setup | Yes | Similar to Kurakin et al. [19], we train on a mixture of normal logit z(x) and the worst logit z(x) as follows: J (f, D; λ) = E(x,y) D (1 λ)L(z(x), y) + λL(z(x), y) i . We gradually increase the perturbation ϵ from 0 to the target bound ϵtarget and increase λ in (13) from 0 to 1, stabilizing the initial phase of training and improving natural accuracy [13, 44]. We use the same batch size for both methods as 50 on MNIST and CIFAR-10 and 5 on Tiny Image Net. In the case of WRN [42] on CIFAR-10, we can speed up to 61.1 sec/epoch using batch size of 128. |