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..
Sparse Maximum Margin Learning from Multimodal Human Behavioral Patterns
Authors: Ervine Zheng, Qi Yu, Zhi Zheng
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world behavioral data from medical and psychological domains demonstrate that our framework discovers meaningful multimodal behavioral patterns with improved interpretability and prediction performance. We conduct experiments on two real-world datasets from medical and psychological domains. |
| Researcher Affiliation | Academia | Ervine Zheng, Qi Yu, Zhi Zheng Rochester Institute of Technology EMAIL |
| Pseudocode | No | The paper describes the inference process but does not include any explicit pseudocode or algorithm blocks in the main text. Details are referred to the Appendix. |
| Open Source Code | Yes | The appendix and source code are presented in (Zheng, Yu, and Zheng 2023). https://github.com/ritmininglab/SM2-MRS/. Accessed: 2023-02-01. |
| Open Datasets | Yes | Dataset II: Behavioral Study in Psychology The second dataset was from a behavioral experiment that studied sensory processing in children with and without Autism Spectrum Disorder (ASD) (Koirala et al. 2021). |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) in the main text needed to reproduce the experiment. It only mentions using datasets and reporting performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |