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 [1].
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. |