Logarithmic Regret for Online Control
Authors: Naman Agarwal, Elad Hazan, Karan Singh
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We presented two algorithms for controlling linear dynamical systems with strongly convex costs with regret that scales poly-logarithmically with time. This improves state-of-the-art known regret bounds that scale as O(T). It remains open to extend the poly-log regret guarantees to more general systems and loss functions, such as exp-concave losses, or alternatively, show that this is impossible. |
| Researcher Affiliation | Collaboration | Naman Agarwal1 Elad Hazan1 2 Karan Singh1 2 1 Google AI Princeton 2 Computer Science, Princeton University namanagarwal@google.com, {ehazan,karans}@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Online Control Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper is theoretical and does not use or describe a publicly available dataset for experiments. It mentions 'noise wt is a random variable generated independently at every time step' which is a theoretical assumption. |
| Dataset Splits | No | The paper is theoretical and does not provide specific details regarding dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |