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..
Understanding the Under-Coverage Bias in Uncertainty Estimation
Authors: Yu Bai, Song Mei, Huan Wang, Caiming Xiong
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on simulated and real data verify our theory and further illustrate the effect of various factors such as sample size and model capacity on the under-coverage bias in more practical setups. |
| Researcher Affiliation | Collaboration | Yu Bai Salesforce Research EMAIL Song Mei UC Berkeley EMAIL Huan Wang Salesforce Research EMAIL Caiming Xiong Salesforce Research EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | Yes | We take six real-world regression datasets: community and crimes (Community) [2], bike sharing (Bike) [1], Tennessee s student teacher achievement ratio (STAR) [6], as well as the medical expenditure survey number 19 (MEPS_19) [3], number 20 (MEPS_20) [4], and number 21 (MEPS_21) [5]. |
| Dataset Splits | Yes | For each setting, we average over 8 random seeds where each seed determines the train-validation split, model initialization, and SGD batching. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using momentum SGD, but does not provide specific software names with version numbers (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x) or specific solver versions. |
| Experiment Setup | Yes | We minimize the α-quantile loss (3) via momentum SGD with batch size 64. |