Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
Authors: Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 spasteris@turing.ac.uk Chris Hicks The Alan Turing Institute London UK c.hicks@turing.ac.uk Vasilios Mavroudis The Alan Turing Institute London UK vmavroudis@turing.ac.uk Mark Herbster University College London London UK m.herbster@cs.ucl.ac.uk |
| 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. |