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 Tree Structured Potential Games
Authors: Vikas Garg, Tommi Jaakkola
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 13,643 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,045 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now describe the results of our experiments on both synthetic and real data to demonstrate the efficacy of our algorithm. |
| Researcher Affiliation | Academia | Vikas K. Garg CSAIL, MIT EMAIL Tommi Jaakkola CSAIL, MIT EMAIL |
| Pseudocode | Yes | Algorithm 1 Learning tree structured potential games |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for its methodology or a link to a code repository. |
| Open Datasets | Yes | Publicly available at http://scdb.wustl.edu/. Publicly available at http://www.senate.gov/. |
| Dataset Splits | No | The paper uses a training set but does not specify any explicit train/validation/test dataset splits, percentages, or cross-validation methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) used in the experiments. |
| Experiment Setup | Yes | We report below the results of our experiments with the following setting of parameters: ρ = 1, βt = 0.005 (for all t), C = 10, ϵ = 0.1, and Max Iter = 100. For each local optimization problem, the configurations were constrained to share the slack variable in order to reduce the total number of optimization variables. Moreover, we used a scaled 0-1 loss [15], e(y, ym) = 1{y = ym}/n for each local optimization. We set h = 1 for the approximate method. |