Name
Abstract | ||
|---|---|---|
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a Mondrian process [Roy and Teh, 2009], and we highlight the connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are also sampled via a Mondrian process, but fit independently. This link provides a new insight into the relationship between kernel methods and random forests. |
Year | Venue | DocType |
|---|---|---|
2016 | UAI | Conference |
Citations | PageRank | References |
1 | 0.38 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| matej balog | 1 | 4 | 1.79 |
| Balaji Lakshminarayanan | 2 | 270 | 21.07 |
| Zoubin Ghahramani | 3 | 10455 | 1264.39 |
| Daniel M. Roy | 4 | 818 | 63.27 |
| Yee Whye Teh | 5 | 6253 | 539.26 |