Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Achieving Privacy in the Adversarial Multi-Armed Bandit
Authors: Aristide Tossou, Christos Dimitrakakis
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 14,708 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,455 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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... |