GBHT: Gradient Boosting Histogram Transform for Density Estimation
Authors: Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |