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..
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Authors: Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real datasets validate that the algorithm can quickly and accurately classify points on a stream with a small quantity of labeled examples. We evaluate our solution on several real datasets. |
| Researcher Affiliation | Collaboration | 1CSAIL, MIT. Work done during an internship at Amazon. 2Amazon. Correspondence to: Tal Wagner <EMAIL>. |
| Pseudocode | Yes | Algorithm TLP : Temporal Label Propagation |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | (a) Incart-ECG (Goldberger et al., 2000): Dataset of ECG timeseries from Physio Net bank (b) Daphnet-Gait (Bachlin et al., 2010): Annotated readings of 9 accelerometer sensors (c) Caltech10-101 (Fei-Fei et al., 2006): Caltech-101 dataset consists of images annotated by 101 object classes (d) Cam Vid-Car (Brostow et al., 2009) (Cambridge-driving Labeled Video Database) |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or explicit references to standard splits) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments were performed on a 3.1 GHz Intel Core i7 machine with 16GB RAM. |
| Software Dependencies | No | The paper mentions using "standard RBF similarity" but does not specify any software libraries, frameworks, or solvers with version numbers that would be necessary for reproduction. |
| Experiment Setup | Yes | We set σ = 0.1 for Incart-ECG, Daphnet-Gait, and Cam Vid and σ = 10 for Caltech10-101. |