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..
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Authors: Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are provided to support our analysis. We demonstrate the effectiveness of our proposed algorithm using both simulated and real datasets. |
| Researcher Affiliation | Academia | Minshuo Chen1 Lin F. Yang2 Mengdi Wang2 Tuo Zhao1 1Georgia Institute of Technology 2Princeton University 1EMAIL 2EMAIL |
| Pseudocode | Yes | Algorithm 1 Downsampled Oja s Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We adopt the Air Quality dataset (De Vito et al., 2008) |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., library names, framework versions, or programming language versions with libraries). |
| Experiment Setup | Yes | The step size is = 3 10 5, and the algorithm runs with 8 105 total samples. Specifically, we set the step size = 0 h 4000 if k < 2 104, = 0 h 8000 if k 2 [2 104, 5 104), = 0 h 48000 if k 2 [5 104, 10 104), and = 0 h 120000 if k 10 104. We choose 0 in {0.125, 0.25, 0.5, 1, 2} and report the final principle angles achieved by different block sizes h in Table 1. |