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..
Tight Rates for Bandit Control Beyond Quadratics
Authors: Y. Jennifer Sun, Zhou Lu
NeurIPS 2024 | Venue PDF | 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 EMAIL Zhou Lu Princeton University EMAIL |
| 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. |