Bayesian Deconditional Kernel Mean Embeddings
Authors: Kelvin Hsu, Fabio Ramos
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 8. Applications and Experiments While DMEs are developed to complement the theoretical framework of KMEs, in this section we describe and demonstrate some of their practical applications with experiments. |
| Researcher Affiliation | Collaboration | 1University of Sydney 2CSIRO, Sydney 3NVIDIA, Seattle. |
| Pseudocode | Yes | Algorithm 8.1 Deconditional Mean Embeddings for LFI |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper describes generating data for its experiments (e.g., 'a dataset of 100 points generated by using the toy process from Rasmussen & Williams (2006)') and mentions using standard LFI benchmarks, but it does not provide concrete access information (link, DOI, repository, or formal citation with authors/year for the specific dataset used) for a publicly available dataset. |
| Dataset Splits | No | The paper does not specify explicit training/validation/test dataset splits, such as percentages, sample counts, or the methodology used for partitioning the data. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper discusses the learning of hyperparameters but does not explicitly provide the specific values for these hyperparameters or other concrete training configurations and system-level settings used in the experiments. |