Shogun Planet

August 15, 2017 10:15 AM

Heiko Strathmann

A determinant-free method to simulate the parameters of large Gaussian fields

Together with Louis Ellam, Iain Murray, and Mark Girolami, we just published a new article on dealing with large Gaussian models. This is slightly related to the open problem around the GMRF model in our Russian Roulette paper back a while ago.

We propose a determinant-free approach for simulation-based Bayesian inference in high-dimensional Gaussian models. We introduce auxiliary variables with covariance equal to the inverse covariance of the model. The joint probability of the auxiliary model can be computed without evaluating determinants, which are often hard to compute in high dimensions. We develop a Markov chain Monte Carlo sampling scheme for the auxiliary model that requires no more than the application of inverse-matrix-square-roots and the solution of linear systems. These operations can be performed at large scales with rational approximations. We provide an empirical study on both synthetic and real-world data for sparse Gaussian processes and for large-scale Gaussian Markov random fields.

Article is here. Unfortunately, the journal is not open-access.

by karlnapf at August 15, 2017 10:15 AM

May 26, 2017 08:26 AM

Heiko Strathmann

Efficient and principled score estimation

New paper online: Score matching goes Nystrom. With guarantees!

We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may be infinite dimensional. The model is learned by fitting the derivative of the log density, the score, thus avoiding the need to compute a normalization constant. We improved the computational efficiency of an earlier solution with a low-rank, Nystr\”om-like solution. The new solution retains the consistency and convergence rates of the full-rank solution (exactly in Fisher distance, and nearly in other distances), with guarantees on the degree of cost and storage reduction. We evaluate the method in experiments on density estimation and in the construction of an adaptive Hamiltonian Monte Carlo sampler. Compared to an existing score learning approach using a denoising autoencoder, our estimator is empirically more data-efficient when estimating the score, runs faster, and has fewer parameters (which can be tuned in a principled and interpretable way), in addition to providing statistical guarantees.

by karlnapf at May 26, 2017 08:26 AM

March 01, 2016 11:15 AM

Heiko Strathmann

Google Summer of Code 2016

Great news: Shogun just got accepted to the GSoC 2016. After our break year in 2015, we are extremely excited to continue our GSoC tradition starting in 2011 (when I first joined Shogun).

If you are a student and wish to spend the summer hacking Machine Learning, guided by a vibrant international community of academics, professionals, and NERDS, then pay us a visit. Oh, and you will receive a cheque over $5000 from Google.

This year, we focus on framework improvements rather than solely adding new algorithms. Consequently, most projects have a heavy focus on packaging and software engineering questions. But there will be Machine Learning too. We are aiming high!

Check our our ideas list and read how to get involved.

by karlnapf at March 01, 2016 11:15 AM

December 24, 2015 08:48 PM

Heiko Strathmann

Adaptive Kernel Sequential Monte Carlo

And when your name is Dino Sejdinovic, you can actually integrate out all steps in feature space…..

Ingmar Schuster wrote a nice blog post about our recently arxived paper draft. It is about using the Kameleon Kernel Adaptive Metropolis-Hastings proposal as an MCMC rejuvenation step in a Sequential Monte Carlo context.

by karlnapf at December 24, 2015 08:48 PM