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.
Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
Authors: Fivos Kalogiannis, Jingming Yan, Ioannis Panageas
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 51,459 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,878 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 | Question: Does the paper conduct EMPIRICAL STUDIES WITH DATA ANALYSIS (experiments, dataset evaluation, performance metrics, or hypothesis validation) rather than purely theoretical work? Answer: [No] Justification: The paper is theoretical in nature and does not include experiments. |
| Researcher Affiliation | Academia | Fivos Kalogiannis University of California, Irvine Archimedes/Athena RC, Greece Jingming Yan University of California, Irvine Ioannis Panageas University of California, Irvine Archimedes/Athena RC, Greece |
| Pseudocode | Yes | Algorithm 1 Independent Stochastic Policy-Nested-Gradient (ISPNG) Algorithm 2 Visitation-Regularized Policy Gradient Algorithm (VIS-REG-PG) |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments, so it does not discuss training datasets or their public availability. |
| Dataset Splits | No | The paper does not include experiments, so it does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not include experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not include experiments, so no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper does not include experiments, so it does not detail specific experimental setup, hyperparameters, or training configurations. |