Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Gaussian Process Neural Additive Models
Authors: Wei Zhang, Brian Barr, John Paisley
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 15,988 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,782 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |