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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Incentivizing Combinatorial Bandit Exploration
Authors: Xinyan Hu, Dung Ngo, Aleksandrs Slivkins, Steven Z. Wu
NeurIPS 2022 | Venue PDF | 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 suf๏ฌcient 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: EMAIL 2University of Minnesota. Email: EMAIL 3Microsoft Research NYC. Email: EMAIL 4Carnegie Mellon University. Email: EMAIL |
| 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. |