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..
Hypothesis Sketching for Online Kernel Selection in Continuous Kernel Space
Authors: Xiao Zhang, Shizhong Liao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed hypothesis sketching approach significantly improves the efficiency of online kernel selection in continuous kernel space while retaining comparable predictive accuracies. |
| Researcher Affiliation | Academia | Xiao Zhang and Shizhong Liao College of Intelligence and Computing, Tianjin University, Tianjin 300350, China EMAIL |
| Pseudocode | Yes | Algorithm 1 OKS-SIL Algorithm |
| Open Source Code | No | The paper does not contain any statement about releasing their source code or a link to a repository for their method. |
| Open Datasets | Yes | For each benchmark dataset4 we merged the training and testing data into a single dataset. ... 4http://www.csie.ntu.edu.cn/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | But in online learning settings, there is no such delineation among training, validation and testing [Diethe and Girolami, 2013; Zhang et al., 2019]. |
| Hardware Specification | Yes | all the algorithms were implemented in R 3.3.2 on a PC with 3.60 GHz Intel Core i7 CPU and 16GB memory. |
| Software Dependencies | Yes | all the algorithms were implemented in R 3.3.2 on a PC with 3.60 GHz Intel Core i7 CPU and 16GB memory. |
| Experiment Setup | Yes | For our OKS-SIL, we set B = 150 for the small datasets (T < 104), B = 200 for the other datasets (T ≥ 104), and ν = 0.9, s = 3 according to the empirical analysis in the subsection Parameter Influence . We used a time-varying stepsize ρt = 1/t at round t for optimal kernel updating, and tuned the stepsize of KOGD in a range 10[−5:+1:0] for all the algorithms. |