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..
Data Poisoning Attacks on Stochastic Bandits
Authors: Fang Liu, Ness Shroff
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our attack strategies by numerical results. Our attack strategies are efficient in forcing the bandit algorithms to pull a target arm at a relatively small cost. Our results expose a significant security threat as bandit algorithms are widely employed in the real world applications. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, 2Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA. |
| Pseudocode | No | The paper describes algorithms and strategies in text and mathematical formulas but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | All the simulations are run in MATLAB and the codes can be found in the supplemental materials. |
| Open Datasets | No | The paper uses simulated data generated according to specified distributions (Gaussian noise, uniformly distributed expected rewards) rather than a pre-existing publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes simulations with a time horizon T (e.g., T=1000 or T=10^5 rounds) but does not specify distinct training, validation, or test dataset splits, as data is generated dynamically in the bandit setting. |
| Hardware Specification | No | The paper states 'All the simulations are run in MATLAB' but provides no specific details about the hardware used, such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions 'All the simulations are run in MATLAB' but does not provide a specific version number for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | The bandit has K = 5 arms and the reward noise is a Gaussian distribution N(0, σ2) with σ = 0.1. We set T = 1000 and the error tolerance to δ = 0.05. Then we set the margin parameter as ξ = 0.001... |