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..
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation
Authors: Alexander Levine, Soheil Feizi4585-4593
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, on MNIST, we can certify the classifications of over 50% of images to be robust to any distortion of at most 8 pixels. [...] In this section, we provide experimental results of the proposed method on MNIST, CIFAR-10, and Image Net. |
| Researcher Affiliation | Academia | Alexander Levine, Soheil Feizi University of Maryland, College Park EMAIL |
| Pseudocode | No | The paper describes procedures and theoretical foundations but does not include a dedicated pseudocode block or algorithm listing. |
| Open Source Code | Yes | Code and supplementary material is available at https://github.com/alevine0/randomizedAblation/. |
| Open Datasets | Yes | Experimentally, on MNIST, we can certify the classifications... In this section, we provide experimental results of the proposed method on MNIST, CIFAR-10, and Image Net. |
| Dataset Splits | No | The paper mentions using a 'test set' for MNIST/CIFAR and the 'ILSVRC2012 validation set' for testing ImageNet, but it does not provide explicit details about standard training/validation/test splits (e.g., percentages or sample counts for validation data) for all datasets needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using ResNet architectures and the Foolbox package, but it does not provide specific version numbers for any software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | Unless otherwise stated, the uncertainty α is 0.05, and 10,000 randomly-ablated samples are used to make each prediction. [...] We use 1,000 and 10,000 samples, respectively, for these two steps. [...] For performance reasons, during training, we ablate the same pixels from all images in a minibatch. We use the same retention constant k during training as at test time. [...] for greyscale images where pixels in S are floating point values between zero and one (i.e. S = [0, 1]), we encode s S as the tuple (s, 1 s), and then encode NULL as (0, 0). |