Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
Authors: Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we fully characterize the minimax regret of switching-constrained online convex optimization. Since it is a theoretical result in nature, the broader impact discussion is not applicable. |
| Researcher Affiliation | Academia | Lin Chen1,2 Qian Yu3 Hannah Lawrence4 Amin Karbasi1 1 Yale University 2 Simons Institute for the Theory of Computing 3 University of Southern California 4 Massachusetts Institute of Technology |
| Pseudocode | No | The paper describes algorithms conceptually, such as a 'mini-batching algorithm' and 'adversarial strategies,' but does not provide any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets. Therefore, no information about training/validation/test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require hardware specifications. No mention of hardware is made. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments or mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup, hyperparameters, or system-level training settings. |