Nonparametric Bayesian inference on multivariate exponential families
Authors: William R Vega-Brown, Marek Doniec, Nicholas G Roy
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our algorithm on several problems and show quantifiable improvement in both speed and performance relative to models based on the Gaussian process. |
| Researcher Affiliation | Academia | William Vega-Brown, Marek Doniec, and Nicholas Roy Massachusetts Institute of Technology Cambridge, MA 02139 {wrvb, doniec, nickroy}@csail.mit.edu |
| Pseudocode | No | The paper contains mathematical derivations and equations but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Second, we test on the motorcycle dataset of Silverman et al. [21]. |
| Dataset Splits | No | The paper mentions using various datasets (synthetic, currency exchanges, motorcycle dataset) but does not specify any training, validation, or test splits (e.g., percentages, sample counts, or predefined split references) needed for reproduction. |
| Hardware Specification | No | The paper mentions implementation details ('implemented in MATLAB', 'optimized c code') but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | Both algorithms were implemented in the MATLAB programming language, with the likelihood functions for the GWP implemented in heavily optimized c code in an effort to ensure a fair competition. |
| Experiment Setup | Yes | In all experiments, we use the squared-exponential kernel k(y, y ) = exp( c / 2 y y 2). We set the kernel scale c by maximum likelihood for each model. |