Multi-way Interacting Regression via Factorization Machines
Authors: Mikhail Yurochkin, XuanLong Nguyen, nikolaos Vasiloglou
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is evaluated with extensive experiments on simulated data and demonstrated to be able to identify meaningful interactions in applications in genetics and retail demand forecasting. |
| Researcher Affiliation | Collaboration | Mikhail Yurochkin Department of Statistics University of Michigan moonfolk@umich.edu Xuan Long Nguyen Department of Statistics University of Michigan xuanlong@umich.edu Nikolaos Vasiloglou Logic Blox nikolaos.vasiloglou@logicblox.com |
| Pseudocode | No | The paper mentions 'MCMC sampler (details of the sampler are in the Supplement)' but does not include pseudocode or algorithm blocks in the main body. |
| Open Source Code | Yes | Code is available at https://github.com/moonfolk/Mi FM. |
| Open Datasets | Yes | Our analysis of the epistasis is based on the data from Himmelstein et al. (2011). |
| Dataset Splits | No | The paper mentions 'Root Mean Squared Error on the held out data' and 'Prediction Accuracy on the Held-out Samples' but does not provide specific percentages, sample counts, or explicit splitting methodology for training/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We will compare Mi FM1 and Mi FM0, both fitted with K = 12 and J = 150 |