2.1k citations · 9.2k across the 77 of their papers we have counts for
6 papers · 1 filter
Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers
ziyu wang, Shakir Mohamed, Nando de Freitas
In this paper we address the widely-experienced difficulty in tuning Hamiltonian-based Monte Carlo samplers. We develop an algorithm that allows for the adaptation of Hamiltonian a…
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Arnaud Doucet, Nando de Freitas, Kevin Murphy +1
Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob…
Reversible Jump MCMC Simulated Annealing for Neural Networks
Christophe Andrieu, Nando de Freitas, Arnaud Doucet
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maxi…
Variational MCMC
Nando de Freitas, Pedro Hojen-Sorensen, Michael I. Jordan +1
We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation. Naive algorithms that use the variational appr…
Herded Gibbs Sampling
Luke Bornn, Yutian Chen, Nando de Freitas +3
The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, t…
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Ziyu Wang, Frank Hutter, Masrour Zoghi +2
Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algo…