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..
An Online Learning Algorithm for Bilinear Models
Authors: Yuanbin Wu, Shiliang Sun
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two sequential labelling tasks give positive results. We conduct experiments on two sequential labelling tasks: word segmentation and text chunking. |
| Researcher Affiliation | Academia | Yuanbin Wu EMAIL Shiliang Sun EMAIL Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology, East China Normal University |
| Pseudocode | Yes | Algorithm 1 Blockwise Coordinate Descent. Algorithm 2 Online Learning of the Bilinear Model. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The data set is from the second SIGHAN Backoff (Emerson, 2005). We conduct experiments on the Co NLL Sharedtask 2000 (Sang & Buchholz, 2000). |
| Dataset Splits | Yes | The value R is selected on a dev set (10% of the pku training set). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'SVM solver' and 'CRF' (crf2, crf1 from crfpp.googlecode.com/) but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The algorithms for comparison are: bol which is the proposed method with T = 20, C = 1, R = 4, bcd which is the blockwise coordinate descent with SVM solver in Shalev-Shwartz & Singer (2006) (C = 1). We also compare state-of-the-art online linear model sp which is the structured perceptron with T = 20, learning rate C = 1. |