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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
(Locally) Differentially Private Combinatorial Semi-Bandits
Authors: Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | we prove the optimal regret bound is Θ( m B2 ln T ε2 ) or Θ( m B2 ln T ε ) respectively, where T is time period, is the gap of rewards and m is the number of base arms, by proposing novel algorithms and matching lower bounds. |
| Researcher Affiliation | Collaboration | 1Key Laboratory of Machine Perception, MOE, School of EECS, Peking University 2Work done while interned at Microsoft Research Asia 3School of Electronics Engineering and Computer Science, Peking University 4Center for Data Science, Peking University 5Microsoft Research Asia, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 CUCB-LDP1; Algorithm 2 CUCB-LDP2; Algorithm 3 CUCB-DP |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is purely theoretical and does not involve empirical evaluation on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is purely theoretical and does not involve empirical evaluation on datasets, thus no training/validation/test dataset splits are provided. |
| Hardware Specification | No | The paper is purely theoretical and does not report on experiments requiring specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is purely theoretical and does not describe experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not describe empirical experimental setup details, such as hyperparameters or system-level training settings. |