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..
large scale canonical correlation analysis with iterative least squares
Authors: Yichao Lu, Dean P. Foster
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments also show that L-CCA outperform other fast CCA approximation schemes on two real datasets. |
| Researcher Affiliation | Collaboration | Yichao Lu University of Pennsylvania EMAIL Dean P. Foster Yahoo Labs, NYC EMAIL |
| Pseudocode | Yes | Algorithm 1 CCA via Iterative LS, Algorithm 2 LING, and Algorithm 3 L-CCA are presented in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of source code for the described methodology. |
| Open Datasets | Yes | The dataset used is the full Wall Street Journal Part of Penn Tree Bank which consists of 1.17 million tokens and a vocabulary size of 43k [18]. |
| Dataset Splits | No | The paper describes the datasets used and performance comparisons but does not explicitly provide specific train/validation/test dataset splits or cross-validation details needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments, only mentioning 'CPU time'. |
| Software Dependencies | No | The paper mentions 'matlab built-in CCA function' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In the experiments we ๏ฌx t1 = 5 and vary t2. In both experiments we ๏ฌx kpc = 100... For RPCCA L-CCA G-CCA we try three different parameter set ups shown in table 1 and the 20 correlations are shown in ๏ฌgure 1. |