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..
Optimal Rates for Bandit Nonstochastic Control
Authors: Y. Jennifer Sun, Stephen Newman, Elad Hazan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We answer in the affirmative, giving an algorithm for bandit LQR and LQG which attains optimal regret (up to logarithmic factors) for both known and unknown systems. |
| Researcher Affiliation | Collaboration | Y. Jennifer Sun Princeton University Google Deep Mind EMAIL Stephen Newman Princeton University EMAIL Elad Hazan Princeton University & Google Deep Mind EMAIL |
| Pseudocode | Yes | Algorithm 1 Ellipsoidal BCO with memory (EBCO-M) Algorithm 2 Ellipsoidal Bandit Perturbation Controller (EBPC) Algorithm 3 System estimation via least squares (Sys Est-LS) |
| Open Source Code | No | The paper does not include any explicit statement about releasing open-source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets, hence no information on dataset availability or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no information on training/test/validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe an experimental setup with hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe an experimental setup with specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, such as hyperparameters or training settings. |