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..
Top Rank Optimization in Linear Time
Authors: Nan Li, Rong Jin, Zhi-Hua Zhou
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical study shows that the proposed approach is highly competitive to the state-of-the-art approaches and is 10-100 times faster. To evaluate the performance of the Top Push algorithm, we conduct a set of experiments on realworld datasets. Table 2 (left column) summarizes the datasets used in our experiments. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824 EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 The Top Push Algorithm |
| Open Source Code | No | The paper does not provide any links to its source code or explicitly state that its code is publicly available. |
| Open Datasets | Yes | Table 2 (left column) summarizes the datasets used in our experiments. Some of them were used in previous studies [1, 31, 3], and others are larger datasets from different domains. |
| Dataset Splits | Yes | In each trial, the dataset is randomly divided into two subsets: 2/3 for training and 1/3 for test. For all algorithms, we set the precision parameter ϵ to 10 4, choose other parameters by 5-fold cross validation (based on the average value of Pos@Top) on training set, and perform the evaluation on test set. |
| Hardware Specification | Yes | All experiments are run on a machine with two Intel Xeon E7 CPUs and 16GB memory. |
| Software Dependencies | Yes | We implement Top Push and Infinite Push using MATLAB, implement AATP using CVX [14], and use LIBLINEAR [11] for LR and cs-SVM... [14] refers to 'CVX: Matlab software for disciplined convex programming, version 2.1'. |
| Experiment Setup | Yes | On each dataset, experiments are run for thirty trials. In each trial, the dataset is randomly divided into two subsets: 2/3 for training and 1/3 for test. For all algorithms, we set the precision parameter ϵ to 10 4, choose other parameters by 5-fold cross validation (based on the average value of Pos@Top) on training set, and perform the evaluation on test set. |