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..
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
Authors: Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct simple experiments that illustrate the utility of our theoretical contribution for boosting robustness. These experiments demonstrate that our algorithm, "-Ro Boost, can boost and improve the robustness of black-box learning algorithms. We describe the setup and the results below. On make_moons with perturbation radius = 0.1, the baseline Linear SVM achieves a robust accuracy of 84.78%, while "-Ro Boost (with 2 rounds of boosting) achieves robust accuracy of 89.86%. On MNIST with perturbation radius = 0.5, the baseline Linear SVM achieves a robust accuracy of 73.9%, while "-Ro Boost (with 2 rounds of boosting) achieves robust accuracy of 80.05%. |
| Researcher Affiliation | Academia | Avrim Blum EMAIL TTI Chicago Omar Montasser EMAIL TTI Chicago Greg Shakhnarovich EMAIL TTI Chicago Hongyang Zhang EMAIL University of Waterloo |
| Pseudocode | Yes | Algorithm 1: "-Ro Boost Boosting barely robust learners. |
| Open Source Code | Yes | We include code to reproduce our MNIST experiments with perturbation radius = 1.0 in Appendix F. |
| Open Datasets | Yes | Datasets. A synthetic binary classi cation dataset (make_moons from scikit-learn), and MNIST (rescaled by dividing by 255, and converted to binary classi cation of odd vs. even). |
| Dataset Splits | No | The paper mentions datasets like make_moons and MNIST, and talks about running Linear SVM on them, but does not specify exact training, validation, or test dataset splits (e.g., 80/10/10 percentage or sample counts). |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'scikit-learn' as a dependency but does not provide specific version numbers for any software components. |
| Experiment Setup | Yes | Perturbation set U. We consider 2 perturbations of some radius . ... On make_moons with perturbation radius = 0.1, ... On MNIST with perturbation radius = 0.5, ... Finally, on MNIST with a bigger perturbation radius = 1.0, ... -Ro Boost (with 2 rounds of boosting) achieves robust accuracy... |