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..
Human Guided Linear Regression With Feature-Level Constraints
Authors: Aubrey Gress, Ian Davidson
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show our formulations outperform natural baselines and prior work (Section 5). ... The methods and data sets we used are described in Tables 1 and 2. We generated the guidance by simulating a domain expert. ... Reported results are the mean of 30 train/test splits. Values in parentheses indicate 95% confidence interval. More extensive experiments and learning curves are presented in the supplementary material. |
| Researcher Affiliation | Academia | Aubrey Gress, Ian Davidson Department of Computer Science, University of California, Davis 1 Shields Avenue, Davis, CA, 95616 {EMAIL, EMAIL} |
| Pseudocode | No | The paper describes methods using mathematical formulations and descriptive text, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code and processed data sets are available at https://github.com/adgress/AAAI2018. |
| Open Datasets | Yes | Boston Housing (Harrison and Rubinfeld 1978; Lichman 2013) ... Wine (Lichman 2013) The UCI wine data set. ... Concrete (Yeh 1998; Lichman 2013) ... Lichman, M. 2013. UCI machine learning repository. |
| Dataset Splits | Yes | Reported results are the mean of 30 train/test splits. Values in parentheses indicate 95% confidence interval. ... For all methods all regularization parameters were tuned on a validation set. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions 'Sci Py s optimization module (Jones et al. 2001)' but does not provide specific version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | For all methods all regularization parameters were tuned on a validation set. ... Specifically, we randomly selected a subset of features, estimate the signs (or relative orderings) of regression coefficients of each feature separately using ordinary least squares on a validation set. i.e. to generate the sign of coefficient i, we solved the problem ˆβi = arg minβi ||Xiβi Y ||2 and used the sign to generate the guidance. |