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..
Training-Time Attacks against K-nearest Neighbors
Authors: Ara Vartanian, Will Rosenbaum, Scott Alfeld
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theoretical bounds and empirically demonstrate the effectiveness and practicality of our methods on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Ara Vartanian1, Will Rosenbaum2, Scott Alfeld2 1 University of Wisconsin Madison 2 Amherst College EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Construct IRs, Algorithm 2: CHOPPA, Algorithm 3: GIT2ACHOPPA. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We consider two real-world datasets. MNIST (Le Cun, Cortes, and Burges (2010)) consists of 28 28 greyscale images of handwritten digits (d = 784). Human Activity Recognition (HAPT) (Anguita et al. (2013)). |
| Dataset Splits | No | The paper mentions using 10,000 and 6,000 points for training from MNIST and HAPT, respectively, and evaluates on a held-out set of size 1,000, but does not specify a separate validation set or split percentages for training/validation/test. |
| Hardware Specification | No | Experiments were run on 128core machines from AWS EC2. This indicates a general type of machine but lacks specific hardware models (e.g., CPU, GPU) or detailed specifications. |
| Software Dependencies | Yes | The QCLPs were solved using Mosek (Ap S (2019)). Pre-processing, plotting, and data analysis were performed with pandas (The Pandas Development Team (2020)), scikit-learn (Pedregosa et al. (2011)) and matplotlib (Hunter (2007)). Mosek is mentioned with a version number 9.2.35. |
| Experiment Setup | Yes | Every attacker uses GIT2ACHOPPA with a budget of b = 1, 5, 10, 20 and a time budget of b minutes. We train a 1NN classi๏ฌer using 10,000 and 6,000 points from MNIST and HAPT, respectively, (1,000 from each class). |