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.
Optimal Regret of Bandits under Differential Privacy
Authors: Achraf Azize, Yulian Wu, Junya Honda, Francesco Orabona, Shinji Ito, Debabrota Basu
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 42,423 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,414 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, our numerical experiments validate that DP-KLUCB and DP-IMED achieve lower regret than the existing ϵ-global DP bandit algorithms. |
| Researcher Affiliation | Academia | Achraf Azize Fair Play Joint Team CREST, ENSAE Paris EMAIL Yulian Wu King Abdullah University of Science & Technology (KAUST) Thuwal 23955-6900, Kingdom of Saudi Arabia EMAIL Junya Honda Kyoto University RIKEN AIP EMAIL Francesco Orabona King Abdullah University for Science & Technology (KAUST) Thuwal 23955-6900, Kingdom of Saudi Arabia EMAIL Shinji Ito The University of Tokyo RIKEN AIP EMAIL Debabrota Basu Univ. Lille, Inria, CNRS Centrale Lille, UMR 9189-CRISt AL EMAIL |
| Pseudocode | Yes | Algorithm 1: DP-KLUCB and DP-IMED |
| Open Source Code | Yes | Also, the full code to reproduce our figures is provided in the supplementary material. |
| Open Datasets | No | As our algorithm is straightforward to code, and the datasets consist of simulated Bernoulli instances, all the experiments could be reproduced using a commercial laptop. Our algorithms are implemented from scratch and are tested on synthetic Bernoulli data. |
| Dataset Splits | No | As our algorithm is straightforward to code, and the datasets consist of simulated Bernoulli instances, all the experiments could be reproduced using a commercial laptop. |
| Hardware Specification | Yes | We implement all the algorithms in Python (version 3.8) and on an 8 core 64-bits Intel i5@1.6 GHz CPU. |
| Software Dependencies | Yes | We implement all the algorithms in Python (version 3.8) and on an 8 core 64-bits Intel i5@1.6 GHz CPU. |
| Experiment Setup | Yes | As in Sajed and Sheffet [2019], Azize and Basu [2022], Hu and Hegde [2022], we consider 4 different 5-arm Bernoulli environments, with specific arm-means choices. We run each algorithm 100 times for T = 10^6. For ϵ = 0.25, we plot the mean regret in Figure 1 for µ1 [0.75, 0.7, 0.7, 0.7, 0.7] in the left and µ2 [0.75, 0.625, 0.5, 0.375, 0.25] in the right. In Appendix G, we present additional results for some other environments under different budgets. ...we chose n0 = 1 and α = 2 for DP-KLUCB and DP-IMED. Also, to comply with the regret analysis in [Azize and Basu, 2022, Sajed and Sheffet, 2019], we chose α = 3.1 in Ada P-KLUCB, and β = 1/T in DP-SE. |