Incentivizing Combinatorial Bandit Exploration
Authors: Xinyan Hu, Dung Ngo, Aleksandrs Slivkins, Steven Z. Wu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that Thompson Sampling, when applied to combinatorial semi-bandits, is incentive-compatible when initialized with a sufficient number of samples of each arm (where this number is determined in advance by the Bayesian prior). Moreover, we design incentive-compatible algorithms for collecting the initial samples. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley. Email: xinyanhu@berkeley.edu 2University of Minnesota. Email: ngo00054@umn.edu 3Microsoft Research NYC. Email: slivkins@microsoft.com 4Carnegie Mellon University. Email: zstevenwu@cmu.edu |
| Pseudocode | No | The paper describes algorithms and their properties (e.g., "We present two such algorithms..."), but it does not include formal pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with datasets for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |