Name
Abstract | ||
|---|---|---|
Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or interpolating between bins thus over or underestimating the true bin integrals. In this paper we propose an elegant method for performing Gaussian Process (GP) regression given such binned data, allowing one to make probabilistic predictions of the latent function which produced the binned data. We look at several applications. First, for differentially private regression; second, to make predictions over other integrals; and third when the input regions are irregularly shaped collections of polytopes. In summary, our method provides an effective way of analysing binned data such that one can use more information from the histogram representation, and thus reconstruct a more useful and precise density for making predictions. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Kriging,Histogram,Regression,Bin,Interpolation,Algorithm,Polytope,Artificial intelligence,Gaussian process,Probabilistic logic,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1809.02010 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Thomas Smith | 1 | 0 | 0.68 |
| Mauricio A. Álvarez | 2 | 165 | 23.80 |
| Neil D. Lawrence | 3 | 3411 | 268.51 |