Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Learning in Stackelberg Games with an Omniscient Follower
Authors: Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael Jordan
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,413 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,907 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. We analyze the sample complexity of regret minimization in this repeated Stackelberg game. We show that depending on the reward structure, the existence of the omniscient follower may change the sample complexity drastically, from constant to exponential, even for linear cooperative Stackelberg games. This poses unique challenges for the learning process of the leader and the subsequent regret analysis. |
| Researcher Affiliation | Academia | 1Department of EECS, University of California, Berkeley, USA. |
| Pseudocode | Yes | Algorithm 1 UCB with side information from expert |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not mention the use of specific datasets for training. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not include specific experimental setup details, hyperparameters, or training configurations. |