Online Control with Adversarial Disturbances
Authors: Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is an efficient algorithm for control which achieves O(T) regret in the setting described above. The paper focuses on theoretical guarantees and mathematical proofs (e.g., Theorem 5.1, Lemma 5.2, Theorem 5.3), and does not present empirical studies, data analysis, or experimental results. |
| Researcher Affiliation | Collaboration | 1Google AI Princeton 2Department of Computer Science, Princeton University 3Allen School of Computer Science and Engineering, University of Washington 4Department of Statistics, University of Washington. |
| Pseudocode | Yes | Algorithm 1 Online Control Algorithm |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., specific repository link, explicit code release statement) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets. Therefore, it does not provide concrete access information for a publicly available or open dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with data. Thus, it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers used for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details like hyperparameters or training configurations. |