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..
Spectral Methods for Supervised Topic Models
Authors: Yining Wang, Jun Zhu
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments on a diverse range of synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the algorithm. |
| Researcher Affiliation | Academia | Machine Learning Department, Carnegie Mellon University, EMAIL Dept. of Comp. Sci. & Tech.; Tsinghua National TNList Lab; State Key Lab of Intell. Tech. & Sys., Tsinghua University, EMAIL |
| Pseudocode | Yes | Algorithm 1 spectral parameter recovery algorithm for s LDA. |
| Open Source Code | No | The paper states, 'Both Gibbs sampling for the s LDA model in Fig. 1 (b) and the proposed spectral recovery algorithm are implemented in C++,' but it does not provide any link or explicit statement about the availability of the source code. |
| Open Datasets | Yes | For real-world data, we use the large-scale dataset built on Amazon movie reviews [16] to demonstrate the practical effectiveness of our algorithm. |
| Dataset Splits | Yes | Error bars indicate the standard deviation of 5-fold cross-validation. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper states that the algorithms are 'implemented in C++' but does not provide specific version numbers for C++ compilers or any other libraries/software dependencies. |
| Experiment Setup | Yes | For our spectral algorithm, the hyperparameters L and T are set to 100, which is sufficiently large for all settings in our experiments. The hyper-parameters used in our Gibbs sampling implementation are the same with the ones used to generate the datasets. |