Tight Rates for Bandit Control Beyond Quadratics
Authors: Y. Jennifer Sun, Zhou Lu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is an algorithm that achieves an O(T) optimal regret for bandit nonstochastic control with strongly-convex and smooth cost functions in the presence of adversarial perturbations, improving the previously known O(T 2/3) regret bound from (Cassel and Koren, 2020). |
| Researcher Affiliation | Academia | Y. Jennifer Sun Princeton University ys7849@princeton.edu Zhou Lu Princeton University zhoul@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Improved Bandit Convex Optimization with Affine Memory; Algorithm 2 Improved Bandit Non-stochastic Control; Algorithm 3 Simple BCO-with-delay |
| Open Source Code | No | The paper does not provide concrete access to source code. It is a theoretical paper that presents algorithms. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies using datasets, therefore no dataset splits for validation are mentioned. |
| 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 list specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental setup with hyperparameters or system-level training settings. |