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 Forecasting of Total-Variation-bounded Sequences
Authors: Dheeraj Baby, Yu-Xiang Wang
NeurIPS 2019 | Venue PDF | 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 EMAIL Yu-Xiang Wang Department of Computer Science UC Santa Barbara EMAIL |
| 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). |