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 [1].
SSMF: Shifting Seasonal Matrix Factorization
Authors: Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our algorithm outperforms state-of-the-art baseline methods by accurately forecasting upcoming events on three real-world data streams. |
| Researcher Affiliation | Academia | Koki Kawabata SANKEN Osaka University EMAIL Siddharth Bhatia National University of Singapore EMAIL Rui Liu National University of Singapore EMAIL Mohit Wadhwa EMAIL Bryan Hooi National University of Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 SSMF |
| Open Source Code | Yes | Reproducibility: Our datasets and source code are publicly available at: https://www.github. com/kokikwbt/ssmf |
| Open Datasets | Yes | Table 2: Dataset Description... NYC-YT.3 https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page NYC-CB.4 https://s3.amazonaws.com/tripdata/index.html DISEASE.5 https://www.tycho.pitt.edu/data/ |
| Dataset Splits | Yes | For SSMF and SMF, we determined a learning rate α in α = {0.1, 0.2, 0.3, 0.4} by cross-validation in each training data. |
| Hardware Specification | Yes | We implemented our algorithm in Python (ver. 3.7.4) and all the experiments were conducted on an Intel Xeon W-2123 3.6GHz quad-core CPU with 128GB of memory and running Linux. |
| Software Dependencies | Yes | We implemented our algorithm in Python (ver. 3.7.4) and all the experiments were conducted on an Intel Xeon W-2123 3.6GHz quad-core CPU with 128GB of memory and running Linux. |
| Experiment Setup | Yes | For TRMF, we searched for the best three regularization coefficients, λI, λAR, λLag, in λ = {0.0001, 0.001, 0.01, 0.1, 1, 10}... For SSMF and SMF, we determined a learning rate α in α = {0.1, 0.2, 0.3, 0.4}... The number of components k was set to 15 among all these methods for a fair comparison. |