Data Poisoning Attacks on Stochastic Bandits
Authors: Fang Liu, Ness Shroff
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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... |