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..
Adversarial Attacks on Stochastic Bandits
Authors: Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Jerry Zhu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we run simulations on attacking -greedy and UCB algorithms to illustrate our theoretical findings. |
| Researcher Affiliation | Collaboration | Kwang-Sung Jun Boston University EMAIL Lihong Li Google Brain EMAIL Yuzhe Ma UW-Madison EMAIL Xiaojin Zhu UW-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Alice s attack against a bandit algorithm |
| Open Source Code | No | No explicit statement or link for open-source code for the methodology. |
| Open Datasets | No | The reward distributions of arms 1 and 2 are N(µ1, σ2) and N(0, σ2), respectively, with µ1 > 0. |
| Dataset Splits | No | We repeat 1000 trials. |
| Hardware Specification | No | No specific hardware details are mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | Yes | We let δ = 0.025. Bob s exploration probability decays as t = 1 t . We run Alice and Bob for T = 105 rounds; this forms one trial. We repeat 1000 trials. ... We let δ = 0.05. ...We perform 100 trials with T = 107 rounds. |