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..
GBHT: Gradient Boosting Histogram Transform for Density Estimation
Authors: Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we not only conduct performance comparisons with the widely used KDE, but also apply GBHT to anomaly detection to showcase a further application of GBHT. |
| Researcher Affiliation | Collaboration | 1Key Lab. of Machine Perception (Mo E), School of EECS, Peking University, China 2Department of Applied Mathematics, University of Twente, The Netherlands 3Pazhou Lab, Guangzhou, China. |
| Pseudocode | Yes | Algorithm 1 Gradient Boosting Histogram Transform (GBHT) for Density Estimation |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing the source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper states using 'real datasets from the UCI repository' but does not provide specific links, DOIs, repository names, or formal citations with author/year for accessing these datasets in the main text. |
| Dataset Splits | Yes | The number of iterations T is set to be 100 and the other two hyper-parameters smin and smax − smin are chosen from {−2 + 0.5k, k = 0, . . . , 8} and {0.5 + 0.5k, k = 0, . . . , 5}, respectively, by 3-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We pick smin from the set {−3 + 0.5k, k = 0, . . . , 12} and smax − smin is chosen from the set {0.5 + 0.5k, k = 0, . . . , 5}. For each T we repeat this procedure for 10 times. The number of iterations T is set to be 100 and the other two hyper-parameters smin and smax − smin are chosen from {−2 + 0.5k, k = 0, . . . , 8} and {0.5 + 0.5k, k = 0, . . . , 5}, respectively, by 3-fold cross-validation. |