Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Asymptotically-Optimal Gaussian Bandits with Side Observations
Authors: Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai
ICML 2022 | Venue PDF | 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. |