Asymptotically-Optimal Gaussian Bandits with Side Observations
Authors: Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we first construct an LP-based asymptotic instance-dependent lower bound on the regret. The LP optimizes the cost (regret) required to reliably estimate the suboptimality gap of each arm. This LP lower bound motivates our main contribution: the first known asymptotically optimal algorithm for this general setting. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas, USA 2Department of Computer Science, University of Texas at Austin, Austin, Texas, USA 3Amazon Science, USA. |
| Pseudocode | Yes | Algorithm 1 Asymptotically-Optimal Algorithm for Gaussian Bandits with Side Observations |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to repositories. |
| Open Datasets | No | The paper is theoretical and does not use or describe any specific dataset for training, nor does it provide information about dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore it does not specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs; it does not include details on experimental setup such as hyperparameters or training configurations. |