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..
Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees
Authors: Darshan Thaker, Paris Giampouras, Rene Vidal
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on digit and face classification demonstrate the effectiveness of the proposed approach. |
| Researcher Affiliation | Academia | 1Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD USA. Correspondence to: Darshan Thaker <EMAIL>, Paris Giampouras <EMAIL>. |
| Pseudocode | Yes | In Algorithm 1 in the Appendix, we provide the details of the active set homotopy algorithm. |
| Open Source Code | No | The paper does not provide a direct link to the source code for the described methodology or state that it is being released. |
| Open Datasets | Yes | In this section, we present experiments on the Extended Yale B Face dataset and the MNIST dataset. |
| Dataset Splits | No | The paper mentions training networks on MNIST and Yale B datasets but does not explicitly provide the train/validation/test splits, only training parameters like epochs, learning rate, and batch size. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'cvxpy package' with 'SCS solver' and 'Advertorch library' but does not specify their version numbers. |
| Experiment Setup | Yes | The network on MNIST is trained using SGD for 50 epochs with learning rate 0.1, momentum 0.5, and batch size 128. The ℓ PGD adversary (ϵ = 0.3) used a step size α = 0.01 and was run for 100 iterations. |