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..
Input-Specific Robustness Certification for Randomized Smoothing
Authors: Ruoxin Chen, Jie Li, Junchi Yan, Ping Li, Bin Sheng6295-6303
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 20,430 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,103 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 | The empirical results on CIFAR-10 and Image Net show that ISS can speed up the certification by more than three times at a limited cost of 0.05 certified radius. Meanwhile, ISS surpasses IAS on the average certified radius across the extensive hyperparameter settings. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University 2 The Hong Kong Polytechnic University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Compute ISS mapping ψISS( ) |
| Open Source Code | Yes | We release our code in https: //github.com/roy-ch/Input-Specific-Certification. |
| Open Datasets | Yes | We evaluate our proposed method ISS on two benchmark datasets: CIFAR-10 (Krizhevsky 2009) and Image Net (Russakovsky et al. 2015). |
| Dataset Splits | No | The paper mentions using test data but does not explicitly provide specific training, validation, and test dataset splits with percentages or sample counts for reproduction. |
| Hardware Specification | Yes | All the experiments are conducted on CPU (16 Intel(R) Xeon(R) Gold 5222 CPU @ 3.80GHz) and GPU (one NVIDIA RTX 2080 Ti). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The hyperparameters are listed in Table 2. |