Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Authors: Asaf Benjamin Cassel, Alon Peled-Cohen, Tomer Koren
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The proof is deferred to the full version of the paper [16]. When asked 'Did you run experiments?', the paper states 'N/A' for all sub-questions related to experiments, indicating a purely theoretical work. |
| Researcher Affiliation | Collaboration | Blavatnik School of Computer Science, Tel Aviv University; acassel@mail.tau.ac.il. School of Electrical Engineering, Tel Aviv University, and Google Tel Aviv; alonco@tauex.tau.ac.il. Blavatnik School of Computer Science, Tel Aviv University, Google Tel Aviv; tkoren@tauex.tau.ac.il. |
| Pseudocode | Yes | Algorithm 1 OCO in Adaptive Linear Control; Algorithm 2 OCO with a hidden linear transform. |
| Open Source Code | No | The paper states 'N/A' for including code, data, and instructions needed to reproduce main experimental results, and no explicit statement or link for open-source code is provided. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper states 'N/A' for hardware specifications in its self-assessment. No specific hardware details are mentioned in the text. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper states 'N/A' for specifying training details like hyperparameters. It is a theoretical paper and does not include details on experimental setup. |