Online Forecasting of Total-Variation-bounded Sequences
Authors: Dheeraj Baby, Yu-Xiang Wang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically validate our results, we conducted a number of numerical simulations that compares the regret of ARROWS, (Restarting) OGD and MA. Figure 2 shows the results on a function with heterogeneous smoothness (see the exact details and more experiments in Appendix B). |
| Researcher Affiliation | Academia | Dheeraj Baby Department of Computer Science UC Santa Barbara dheeraj@ucsb.edu Yu-Xiang Wang Department of Computer Science UC Santa Barbara yuxiangw@cs.ucsb.edu |
| Pseudocode | Yes | ARROWS: inputs observed y values, time horizon n, std deviation σ, δ (0, 1], a hyperparameter β > 24 |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code available or include a link to a code repository. |
| Open Datasets | No | The paper discusses simulations on "sequences" and "functions" with "heterogeneous smoothness" but does not refer to any named public datasets or provide links/citations for data used in simulations. |
| Dataset Splits | No | The paper does not specify any dataset splits (e.g., percentages or sample counts for training, validation, or testing) for its simulations. |
| Hardware Specification | No | The paper does not specify any hardware used for running the simulations or experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments or implementation. |
| Experiment Setup | Yes | ARROWS: inputs observed y values, time horizon n, std deviation σ, δ (0, 1], a hyperparameter β > 24. The hyperparameters selected according to their theoretical optimal choice for the TV class (See Theorem 11, 12 for OGD and MA in Appendix C). |