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..
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
Authors: Jeremiah Liu, Brent Coull
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the finite-sample performance of our test under different data-generating functions and estimation strategies for the null model. Our results reveal interesting connections between notions in machine learning (model underfit/overfit) and those in statistical inference (i.e. Type I error/power of hypothesis test), and also highlight unexpected consequences of common model estimating strategies (e.g. estimating kernel hyperparameters using maximum likelihood estimation) on model inference. |
| Researcher Affiliation | Academia | Jeremiah Zhe Liu, Brent Coull Department of Biostatistics Harvard University Cambridge, MA 02138 {zhl112@mail, bcoull@hsph}.harvard.edu |
| Pseudocode | Yes | Algorithm 1 Variance Component Test for h H0 |
| Open Source Code | No | The paper does not include any statements about releasing its source code or provide a link to a code repository. |
| Open Datasets | No | We generate two groups of input features (xi,1, xi,2) Rp1 Rp2 independently from standard Gaussian distribution, representing normalized data representing subject s level of exposure to p1 environmental pollutants and the levels of a subject s intake of p2 nutrients during the study. This indicates simulated data, not a publicly available dataset. |
| Dataset Splits | Yes | LOOCV(λ|kd) = (I diag(Ad,λ)) 1(y ˆhd,λ) where Ad,λ = Kd(Kd + λI) 1. We denote estimate the final LOOCV error for dth kernel ˆϵd = LOOCV ˆλd|kd . Using the estimated LOOCV errors {ˆϵd}D d=1, estimate the ensemble weights u = {ud}D d=1 such that it minimizes the overall LOOCV error: ˆu = argmin u ||d=1 udˆϵd||2 where = {u|u 0, ||u||2 2 = 1} |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Throughout the simulation scenarios, we keep n = 100, and p1 = p2 = 5. We generate the outcome yi as: yi = h1(xi,1) + h2(xi,2) + δ h12(xi,1, xi,2) + ϵi (12) where h1, h2, h12 are sampled from RKHSs H1, H2 and H1 H2, generated using a ground-truth kernel ktrue. We standardized all sampled functions to have unit norm, so that δ represents the strength of interaction relative to the main effect. For each simulation scenario, we first generated data using δ and ktrue as above, then selected a kmodel to estimate the null model and obtain p-value using Algorithm 1. We repeated each scenario 1000 times. |