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..
Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
Authors: Yun Liao, Junfan Li, Shizhong Liao, Qinghua Hu, Jianwu Dang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Ahpatron outperforms the state-of-the-art algorithms on the same or a smaller budget. Table 2: Comparison with the state-of-the-art algorithms |
| Researcher Affiliation | Academia | Yun Liao, Junfan Li, Shizhong Liao , Qinghua Hu, Jianwu Dang College of Intelligence and Computing, Tianjin University, Tianjin 300350, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: AVP, Algorithm 2: Ahpatron |
| Open Source Code | Yes | Codes and datasets: https://github.com/alg4ml/Ahpatron.git |
| Open Datasets | Yes | We download six binary classification datasets from UCI machine learning repository 4 and LIBSVM website 5, as shown in Table 1. 4http://archive.ics.uci.edu/ml/datasets.php 5https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ binary.html |
| Dataset Splits | No | The paper does not specify exact train/validation/test splits or percentages. It describes online learning where examples are processed sequentially, which typically does not involve fixed validation splits in the same manner as batch learning. |
| Hardware Specification | Yes | All algorithms are implemented in R on a Windows machine with 2.8 GHz Core(TM) i7-1165G7 CPU 6. |
| Software Dependencies | No | The paper states "All algorithms are implemented in R" but does not provide a specific version number for R or any other software dependencies with their versions. |
| Experiment Setup | Yes | For BOGD++, NOGD, and FOGD, we choose the stepsize of gradient descent from n 10[ 3:1:3] o . The other parameters of BOGD++ and NOGD follow the original paper. All parameters of POMDR also follow the original paper. For Projectron and Projectron++, there is a parameter 0 < η < 1 balancing the memory costs and prediction performance. We choose η {0.1, 0.9}. For Ahpatron, we set the parameters following Theorem 6, that is, η = 0.0005, λ = U B 2 . We choose the best ε {0.5, 0.6, 0.7, 0.8, 0.9} in hindsight, and set σ = 1 for all datasets. If the per-round running time of Projectron++ is larger than 1 hour, then we set σ = 2. |