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..
Completing State Representations using Spectral Learning
Authors: Nan Jiang, Alex Kulesza, Satinder Singh
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on synthetic HMMs, an aircraft identification domain, and a gene splice dataset shows that, even with weak domain knowledge, the algorithm can significantly outperform standard PSR learning. |
| Researcher Affiliation | Collaboration | UIUC Urbana, IL EMAIL Alex Kulesza Google Research New York, NY EMAIL Satinder Singh University of Michigan Ann Arbor, MI EMAIL |
| Pseudocode | Yes | Algorithm 1 Template for learning transformed PSR-fs; Algorithm 2 A basic spectral algorithm for PSR-f; Algorithm 3 Canonical angle algorithm for PSR-f |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology, such as a repository link, or an explicit statement about code release in supplementary materials. |
| Open Datasets | Yes | Finally, we experiment on a gene splice dataset [13]. ... [13] Dua Dheeru and EfiKarra Taniskidou. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | The hyperparameter d for Algorithm 3 is tuned by 3-fold cross validation on training data, and λ is set to 100 to ensure a succinct model (see Appendix E.1). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The hyperparameter d for Algorithm 3 is tuned by 3-fold cross validation on training data, and λ is set to 100 to ensure a succinct model (see Appendix E.1). |