Gaussian Process Neural Additive Models
Authors: Wei Zhang, Brian Barr, John Paisley
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with several tabular data sets. A key feature of our model is its reduction in parameters and convex optimization, while still providing competitive performance as a neural additive model. |
| Researcher Affiliation | Collaboration | 1Columbia University, New York, NY, USA 2Capital One, New York, NY, USA {wz2363, jwp2128}@columbia.edu, brian.barr@capitalone.com |
| Pseudocode | Yes | Algorithm 1: GP-NAM for regression and classficiation |
| Open Source Code | Yes | The GP-NAM code for regression and classification can be found at https://github.com/Wei2624/GPNAM |
| Open Datasets | Yes | We experiment using several tabular data sets. This includes CA Housing1, FICO2, for which we follow the processing of Radenovic, Dubey, and Mahajan (2022). We also report performance on MIMIC-II3, MIMIC-II4, Credit5, Click6, Microsoft7, Year8 and Yahoo9, Churn10, Adult11, Bikeshare12 tabular data sets. For these, we follow the processing in Chang, Caruana, and Goldenberg (2021); Popov, Morozov, and Babenko (2019). We also consider our own processing of credit lending data sets LCD13 and GMSC13 More information about these data sets is shown in Table 2. |
| Dataset Splits | Yes | Table 2: Statistics from the tabular data sets used in binary classification (top) and regression (bottom) experiments. Dataset #Train #Val #Test |
| Hardware Specification | No | The paper mentions running experiments 'on GPU' and 'on our CPU' but does not specify exact models or types of hardware. |
| Software Dependencies | No | The paper states 'All models are implemented with Py Torch' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Since each GP is one dimensional, we empirically found that S = 100 works well. |