Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Parallel Pareto Optimization for Subset Selection
Authors: Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,351 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,104 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies verify the effectiveness of PPOSS, and moreover suggest that the asynchronous implementation is more efficient with little quality loss. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2UBRI, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China |
| Pseudocode | Yes | Algorithm 1 POSS Algorithm 2 Parallel POSS (PPOSS) |
| Open Source Code | No | The paper does not provide a specific repository link, an explicit code release statement, or indicate code availability in supplementary materials for the methodology described. |
| Open Datasets | Yes | The data sets are from http://archive.ics.uci.edu/ml/ and http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | The paper mentions using 6 datasets but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for training, validation, and testing. |
| Hardware Specification | Yes | All of the experiments are coded in Java and run on an identical configuration: a server with 4 Intel(R) Xeon(R) CPU E5-2640 v2 (8 real cores each, 20M Cache, 2.00GHz, hyper-threading) and 32GB of RAM. The kernel is Linux 2.6.18-194.el5. |
| Software Dependencies | No | The paper states that experiments are 'coded in Java' and mentions 'Linux 2.6.18-194.el5' for the kernel, but it does not provide specific ancillary software details, such as Java version or specific library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set the sparsity k = 8, and for PPOSS, we use the setting suggested by Theorem 1: T = b2ek2n/Nc. We test the number of cores N from 1 to 10. |