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 [1].
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
Authors: Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: This paper does not include experiments. Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [Yes] Justification: All theorems are either referenced or proved. Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [NA] Justification: This work is theoretical in nature and we cannot foresee any societal impacts. |
| Researcher Affiliation | Academia | Stephen Pasteris The Alan Turing Institute London UK EMAIL Chris Hicks The Alan Turing Institute London UK EMAIL Vasilios Mavroudis The Alan Turing Institute London UK EMAIL Mark Herbster University College London London UK EMAIL |
| Pseudocode | Yes | Algorithm 1 RESET |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: This paper does not include experiments. |
| Open Datasets | No | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: This paper does not include experiments. |
| Dataset Splits | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: This paper does not include experiments. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: This paper does not include experiments. |
| Software Dependencies | No | The paper does not include any experiments that would require software dependencies for reproducibility. The NeurIPS checklist explicitly states that the paper does not include experiments. |
| Experiment Setup | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: This paper does not include experiments. |