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 Adversarial Bandits
Authors: Yuzhe Ma, Zhijin Zhou
ICLR 2023 | Venue PDF | 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 EMAIL Zhijin Zhou Amazon EMAIL |
| 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. |