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..
On the Sublinear Regret of GP-UCB
Authors: Justin Whitehouse, Aaditya Ramdas, Steven Z. Wu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we resolve this open question and show that GP-UCB enjoys nearly optimal regret. In particular, our results yield sublinear regret rates for the Mat ern kernel, improving over the state-of-the-art analyses and partially resolving a COLT open problem posed by Vakili et al. Our improvements rely on a key technical contribution regularizing kernel ridge estimators in proportion to the smoothness of the underlying kernel k. Applying this key idea together with a largely overlooked concentration result in separable Hilbert spaces (for which we provide an independent, simplified derivation), we are able to provide a tighter analysis of the GP-UCB algorithm. |
| Researcher Affiliation | Academia | Justin Whitehouse Carnegie Mellon University EMAIL Zhiwei Steven Wu Carnegie Mellon University EMAIL Aaditya Ramdas Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Gaussian Process Upper Confidence Bound (GP-UCB) |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus it does not provide information about public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not discuss specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameter values or training configurations. |