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 [1].
Achieving Privacy in the Adversarial Multi-Armed Bandit
Authors: Aristide Tossou, Christos Dimitrakakis
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis. |
| Researcher Affiliation | Academia | Aristide C. Y. Tossou Chalmers University of Technology Gothenburg, Sweden EMAIL Christos Dimitrakakis University of Lille, France Chalmers University of Technology, Sweden Harvard University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 DP-EXP3-Lap |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | No | The paper describes generating gains based on different adversary models (e.g., 'Bern (0.55)', 'Bern (0.5)') rather than using a pre-existing publicly available dataset, and no access information for such generated data is provided. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, as it operates in an online adversarial multi-armed bandit setting where gains are generated dynamically rather than from a pre-existing dataset with fixed splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For all experiments, the horizon is T = 2^18 and the number of arms is K = 4. We performed 720 independent trials and reported the median-of-means estimator... We set the number of groups to a0 = 24... |