Online robust non-stationary estimation
Authors: Abishek Sankararaman, Balakrishnan Narayanaswamy
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 | {abisanka, muralibn}@amazon.com, 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. |