Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Authors: Motonobu Kanagawa, Philipp Hennig
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,405 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,711 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL & EMAIL |
| 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. |