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..
Stackelberg Learning with Outcome-based Payment
Authors: Tom Yan, Chicheng Zhang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical paper that does not include experiments. |
| Researcher Affiliation | Academia | Tom Yan Carnegie Mellon University EMAIL Chicheng Zhang University of Arizona EMAIL |
| Pseudocode | Yes | Algorithm 1 Planning Algorithm for MDP with Deterministic Tree Structure |
| Open Source Code | No | The paper does not include experiments requiring code. There is no explicit statement or link indicating the release of source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not include experiments. Therefore, no datasets are used or made available. |
| Dataset Splits | No | This is a theoretical paper that does not include experiments. Therefore, no dataset splits are provided. |
| Hardware Specification | No | This is a theoretical paper that does not include experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not include experiments. Therefore, no specific software dependencies with version numbers for replication are mentioned. |
| Experiment Setup | No | This is a theoretical paper that does not include experiments. Therefore, no specific experimental setup details or hyperparameters are provided. |