1k citations · 5.3k across the 132 of their papers we have counts for
8 papers · 2 filters
Poisson Random Fields for Dynamic Feature Models
Valerio Perrone, Paul A. Jenkins, Dario Spano +1
We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features. T…
Gaussian Processes for Survival Analysis
Tamara Fernández, Nicolás Rivera, Yee Whye Teh
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from…
Poisson intensity estimation with reproducing kernels
Seth Flaxman, Yee Whye Teh, Dino Sejdinovic
Despite the fundamental nature of the inhomogeneous Poisson process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), fe…
Relativistic Monte Carlo
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever +2
Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Ha…
Bayesian Nonparametrics for Sparse Dynamic Networks
Cian Naik, Francois Caron, Judith Rousseau +2
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the…
The Mondrian Kernel
Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani +2
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-selec…