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..
Uncertainty Regularized Evidential Regression
Authors: Kai Ye, Tiejin Chen, Hua Wei, Liang Zhan
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments substantiate our theoretical findings and demonstrate the effectiveness of the proposed solution. |
| Researcher Affiliation | Academia | 1University of Pittsburgh, Pittsburgh, PA, 15260, USA 2Arizona State University, Tempe, AZ, 85281, USA |
| Pseudocode | No | The paper describes its methods mathematically and textually but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code is at https://github.com/FlynnYe/UR-ERN |
| Open Datasets | Yes | Following (Amini et al. 2020), we train models on y = x3 + ϵ, where ϵ N(0, 3). We conduct training over the interval x [ 4, 4], and perform testing over x [ 6, 4) (4, 6]." and "We choose the NYU Depth v2 dataset (Silberman et al. 2012) for experiments. |
| Dataset Splits | No | The paper mentions training and testing intervals for the cubic regression dataset, but it does not explicitly provide training/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation details) for reproducibility, nor does it specify how validation data was used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') that would be necessary to replicate the experiment environment. |
| Experiment Setup | Yes | For experiments within HUA, we initialize the model within HUA by setting bias in the activation layer." and "Please refer to Appendix B for details about experimental setups and experiments about the sensitivity of hyperparameters. |