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..
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Authors: Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section reports an in-depth validation of the proposed method on a nonlinear function approximation task with complex (heterogeneous and heavy-tailed) observation noise. |
| Researcher Affiliation | Collaboration | Jeremiah Zhe Liu Google Research & Harvard University EMAIL John Paisley Columbia University EMAIL Marianthi-Anna Kioumourtzoglou Columbia University EMAIL Brent A. Coull Harvard University EMAIL |
| Pseudocode | No | The paper describes methods and processes in narrative text (e.g., Section 2 and Section C) but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | We apply BNE to a real-world air pollution prediction ensemble system in Eastern Massachusetts consisted of three state-of-the-art PM2.5 exposure models ([23, 16, 47]). We implement our ensemble framework on the base models out-of-sample predictions at 43 monitors in Eastern Massachusetts in 2011. |
| Dataset Splits | No | We vary sample size between 100 and 1000, and repeat the simulation 50 times in each setting. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or specific computing environments) used for running its experiments. |
| Software Dependencies | No | Posterior sampling is performed using Hamiltonian Monte Carlo (HMC) [2] |
| Experiment Setup | No | The detailed experiment settings are documented in Supplementary G. |