Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Authors: Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 zhl112@mail.harvard.edu John Paisley Columbia University jpaisley@columbia.edu Marianthi-Anna Kioumourtzoglou Columbia University mk3961@cumc.columbia.edu Brent A. Coull Harvard University bcoull@hsph.harvard.edu |
| 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. |