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..
SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method
Authors: Zhijian Li, Chao Zhang, Hui Qian, Xin Du, Lingwei Peng
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data validate our theory and demonstrate the superiority of SHPOS over the state-of-the-art. We evaluate our method on a list of tasks, including both synthetic and real datasets. The empirical results demonstrate the superiority of our method over the state-of-the-art. |
| Researcher Affiliation | Collaboration | Zhijian Li1 , Chao Zhang2,3 , Hui Qian2,3 , Xin Du1 and Lingwei Peng2 1Information Science and Electronic Engineering, Zhejiang University 2College of Computer Science and Technology, Zhejiang University 3Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies EMAIL |
| Pseudocode | Yes | Algorithm 1 Stochastic Hamiltonian Particle Optimization Sampling |
| Open Source Code | No | The paper does not provide a statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Four publicly available benchmark datasets from LIBSVM1, a3a, w8a, a8a, and ijcnn1 are used for evaluation. ... 1https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ ... 6 datasets from UCI2 and LIBSVM. ... 2http://archive.ics.uci.edu/ml/datasets.php |
| Dataset Splits | No | The paper mentions using datasets for evaluation but does not specify training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use 1000 particles that initialized by drawing from a Gaussian distribution with mean [ 4, 2]T and variance 0.252. ... We report negative log-likelihood versus the number of data passes with 50 particles on datasets a3a and w8a ... we use a Gamma(1, 0.1) prior for the inverse covariance and adopt a one-hidden-layer neural network with 50 hidden units. |