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..
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Authors: Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper initiates the theoretical study of sample-ef๏ฌcient learning of the Stackelberg equilibrium, in the bandit feedback setting where we only observe noisy samples of the reward. |
| Researcher Affiliation | Collaboration | Yu Bai Salesforce Research EMAIL Chi Jin Princeton University EMAIL Huan Wang Salesforce Research EMAIL Caiming Xiong Salesforce Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Learning Stackelberg in bandit games |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on sample complexity. It does not use or refer to specific publicly available datasets for training empirical models. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail concrete experimental setup parameters like hyperparameters or training configurations for empirical runs. |