On Prior Distributions and Approximate Inference for Structured Variables
Authors: Oluwasanmi O Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 sanmi@stanford.edu Rajiv Khanna ECE Dept., UT Austin rajivak@utexas.edu Joydeep Ghosh ECE Dept., UT Austin ghosh@ece.utexas.edu Russell A. Poldrack Psychology Dept., Stanford poldrack@stanford.edu |
| 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. |