Adversarial Attacks on Stochastic Bandits
Authors: Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Jerry Zhu
NeurIPS 2018 | Venue PDF | Archive PDF | Plain Text | 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. |