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.
On the inability of Gaussian process regression to optimally learn compositional functions
Authors: Matteo Giordano, Kolyan Ray, Johannes Schmidt-Hieber
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,936 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,796 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 | We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for posterior contraction rates for Gaussian process regression in a continuous regression model. |
| Researcher Affiliation | Academia | Matteo Giordano Department of Statistics University of Oxford EMAIL Kolyan Ray Department of Mathematics Imperial College London EMAIL Johannes Schmidt-Hieber Department of Applied Mathematics University of Twente EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making source code for the described methodology publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets, therefore it does not mention publicly available training datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies, therefore it does not provide training/test/validation dataset splits. |
| 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 provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |