Adversarial Attacks on Adversarial Bandits
Authors: Yuzhe Ma, Zhijin Zhou
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our proposed attack algorithms are efficient on both vanilla and a robust version of Exp3 algorithm Yang et al. (2020). |
| Researcher Affiliation | Industry | Yuzhe Ma Microsoft Azure AI yuzhema@microsoft.com Zhijin Zhou Amazon zhijin@amazon.com |
| Pseudocode | No | The paper references 'Exp3 algorithm (see algorithm 1 in the appendix)', but the appendix is not included in the provided text. Therefore, pseudocode is not present in the main paper. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository for its methodology. |
| Open Datasets | No | The paper describes a synthetic bandit problem setup with 'K = 2 arms' and custom loss functions. It does not refer to any publicly available or open dataset used for training, nor does it provide access information for any dataset. |
| Dataset Splits | No | The paper describes synthetic experimental scenarios with varying total horizon T, but it does not specify explicit training, validation, or test dataset splits. The experiments are based on simulations over a total number of rounds T. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In our first example, we consider a bandit problem with K = 2 arms, a1 and a2. The loss function is 8t, Lt(a1) = 0.5 and Lt(a2) = 0. ... In the first experiment, we let the total horizon be T = 103, 104, 105 and 106. ... For the other victim Exp Rb, we consider different levels of attack budget Φ. ... we consider Φ = T 0.5, T 0.7 and T 0.9. ... Next we apply the general attack (6) to verify that (6) can recover the results of Theorem 4.3 in the easy attack scenario. We fix = 0.25 in (6). ... In our second example, we consider a bandit problem with K = 2 arms and the loss function is 8t, Lt(a1) = 1 and Lt(a2) = 0. ... We let = 0.1, 0.25 and 0.4. |