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.
Neural Stochastic Differential Games for Time-series Analysis
Authors: Sungwoo Park, Byoungwoo Park, Moontae Lee, Changhee Lee
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 30,617 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,507 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 | Throughout the experiments on various datasets, we demonstrate the superiority of our framework over all the tested benchmarks in modeling time-series prediction by capitalizing on the advantages of applying cooperative games. |
| Researcher Affiliation | Collaboration | 1LG AI Research 2Artificial Intelligence Graduate School, Chung-Ang University 3Department of Information and Decision Sciences, University of Illinois Chicago. Correspondence to: Sungwoo Park <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Deep Neural Fictitious Play |
| Open Source Code | Yes | Our code is available at https://github.com/LGAI-AML/Ma SDEs. |
| Open Datasets | Yes | We evaluated the time-series prediction performance of ours and the benchmarks on multiple real-world datasets: BAQD (Zhang et al., 2017), Speech (Warden, 2018), and Physionet (Silva et al., 2012). |
| Dataset Splits | No | We split each time-series in the interval [0, T] into two sub-intervals: the first 80% as the observation interval, i.e., O = [0, 0.8T], and the remaining 20% as the prediction interval, i.e., T = [0.8T, T]. We split time-series samples into two halves as training/evaluation sets in Physionet. For BAQD and Speech datasets, we divided time-series samples into 80/20 training/testing splits for training and evaluation, respectively. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names or solver names with specific versions) were mentioned. |
| Experiment Setup | Yes | In all experiments with real-world datasets, we train each model for 500 epochs using the Adam optimizer with a learning rate of 10^-3 and batch size of 128. |