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..
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
Authors: Ching-Pei Lee, Dan Roth
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments also show that our method is significantly faster than state-of-the-art distributed linear SVM algorithms including DSVM-AVE, Dis DCA and TRON. [...] 4. Experiments The following algorithms are compared in our experiments. |
| Researcher Affiliation | Academia | Ching-pei Lee EMAIL Dan Roth EMAIL University of Illinois at Urbana-Champaign, 201 N. Goodwin Avenue, Urbana, IL 61801 USA |
| Pseudocode | Yes | Algorithm 1 A box-constrained quadratic optimization algorithm for distributedly solving (2) |
| Open Source Code | Yes | The code used in the experiments is available at http://github.com/leepei/distcd_exp/. |
| Open Datasets | Yes | The statistics of the data sets in our experiments are shown in Table 1. All of them are publicly available.3 [Footnote 3: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets.] |
| Dataset Splits | Yes | For webspam and url, test sets are not available so we randomly split the original data into 80%/20% as training set and test set, respectively. |
| Hardware Specification | Yes | We use 16 nodes in a cluster. Each node has two Intel HP X5650 2.66GHZ 6C Processors, and one core per node is used. |
| Software Dependencies | Yes | We use the package MPI-LIBLINEAR 1.96.2 |
| Experiment Setup | Yes | We fix C = 1 in all experiments for a fair comparison in optimization. [...] In BQO-E and BQO-A, τ = 0.001 is used. [...] We follow Tseng & Yun (2009) to use β = 0.5, σ = 0.1, γ = 0. |