Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Authors: Motonobu Kanagawa, Philipp Hennig
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, for a broad class of adaptive Bayesian quadrature methods, we prove consistency, deriving non-tight but informative convergence rates. To do so we introduce a new concept we call weak adaptivity. Our results identify a large and flexible class of adaptive Bayesian quadrature rules as consistent, within which practitioners can develop empirically efficient methods. |
| Researcher Affiliation | Academia | Motonobu Kanagawa ,# and Philipp Hennig# EURECOM, Sophia Antipolis, France #University of Tübingen and Max Planck Institute for Intelligent Systems, Tübingen, Germany motonobu.kanagawa@eurecom.fr & philipp.hennig@uni-tuebingen.de |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. Figure 1 illustrates the relationships between auxiliary results and main results, not an algorithm. |
| Open Source Code | No | The paper does not provide any statements or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not use any specific publicly available datasets for empirical training or evaluation. The mention of |
| Dataset Splits | No | This paper is theoretical and does not describe any empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe any experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not describe any experiments; therefore, no software dependencies with specific version numbers are listed. |
| Experiment Setup | No | This paper is theoretical and does not describe any experiments or their setup, including hyperparameters or training configurations. |