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..
Online robust non-stationary estimation
Authors: Abishek Sankararaman, Balakrishnan Narayanaswamy
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results empirically on synthetic and real data. and We complement our theoretical results empirically on synthetic and real data. and 7 Experiments |
| Researcher Affiliation | Industry | EMAIL, Amazon Web Services, Santa Clara CA, USA. |
| Pseudocode | Yes | Algorithm 1 Clipped-SGD and Algorithm 2 Online-Clipped-SGD without time horizon |
| Open Source Code | No | No explicit statement about providing source code or a link to a code repository found. |
| Open Datasets | Yes | Dataset Stream-length T Task Dimension d Electricity NSW [26, 4] 45312 Binary classification 13 Mini Boone [49, 4] 130065 Binary Classification 50 MNIST [34] 11811 Anomaly Detection 784 Table 3: Real data-sets used. (Citations [4], [26], [34], [49] point to publicly available datasets like UCI repository and MNIST.) |
| Dataset Splits | No | No explicit mention of training, validation, or test dataset splits with percentages or sample counts for reproducibility. The evaluation is described as an online streaming process. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) are provided for reproducibility. |
| Experiment Setup | Yes | We compare clipped-SGD with learning rates ηt := 1/m T α and clipping values λ = 2T β for a variety of α and β. and For the case of α = 1, we use the learning rate of ηt = 1/(m(t + 1)) and λ = 2T as suggested in [55]. and We set the gradient clipping value λ = 1 for both datasets. and consider clipped SGD with clip-value set to 5 for various learning rates as shown in Figure 5c. |