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..
On Prior Distributions and Approximate Inference for Structured Variables
Authors: Oluwasanmi O Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on simulated data and high dimensional neuroimaging data show the effectiveness of our approach in terms of support recovery and predictive accuracy. |
| Researcher Affiliation | Academia | Oluwasanmi Koyejo Psychology Dept., Stanford EMAIL Rajiv Khanna ECE Dept., UT Austin EMAIL Joydeep Ghosh ECE Dept., UT Austin EMAIL Russell A. Poldrack Psychology Dept., Stanford EMAIL |
| Pseudocode | No | The paper describes algorithms (e.g., greedy forward selection) but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper mentions using publicly available implementations for baselines (scikit-learn, Spike and Slab) but does not provide access to its own source code. |
| Open Datasets | Yes | FMRI data were collected from 126 participants while the subjects performed a stop-signal task [24]. ... f MRI data was provided by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083269, RL1DA024853, PL1MH083271). |
| Dataset Splits | Yes | For each of these models, the hyperparameter was selected in an inner 5-fold cross validation loop. |
| Hardware Specification | No | The paper does not explicitly specify the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'scikit-learn python package [20]' and 'publicly available implementation of Spike and Slab [21]' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The noise variance hyperparameter for Ridge and ARD were selected from the set 10{ 4, 3,...,4}. Lasso was evaluated using the default scikit-learn implementation where the hyperparameter is selected from 100 logarithmically spaced values based on the maximum correlation between the features and the response. |