Skip to content

matejbalog/mondrian-kernel

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

The Mondrian Kernel

Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), 2016.

[PDF] [supp] [arXiv] [poster] [slides]

The scripts provided here implement experiments from this paper. The scripts experiment_1_laplace_kernel_approximation, experiment_2_fast_kernel_width_learning and experiment_3_mondrian_kernel_vs_forest are intended to be directly runnable.

Requirements

Python packages: heapq, matplotlib, numpy, scipy, sklearn, sys, time

The CPU dataset cpu.mat can be download and extracted from here.

Known issues

A bug in scipy may cause the Python kernel to restart when loading the CPU dataset from cpu.mat. Downgrading to scipy 0.16.0 should solve the problem.

BibTeX

@inproceedings{balog2016mondriankernel,
  author = {Matej Balog and Balaji Lakshminarayanan and Zoubin Ghahramani and Daniel M.~Roy and Yee Whye Teh},
  title={The {M}ondrian Kernel},
  booktitle = {32nd Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2016},
  month = {June},
  url = {http://www.auai.org/uai2016/proceedings/papers/236.pdf}
}

About

The Mondrian kernel is a random feature approximation to the Laplace kernel allowing fast kernel width selection.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages