1.5k citations · 2.5k across the 10 of their papers we have counts for
10 papers
Low-rank extended Kalman filtering for online learning of neural networks from streaming data
Peter G. Chang, Gerardo Durán-Martín, Alexander Y Shestopaloff +2
We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The met…
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
Zeel B Patel, Nipun Batra, Kevin Murphy
Gaussian processes are Bayesian non-parametric models used in many areas. In this work, we propose a Non-stationary Heteroscedastic Gaussian process model which can be learned with…
Learning the Structure of Dynamic Probabilistic Networks
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend struc…
Loopy Belief Propagation for Approximate Inference: An Empirical Study
Kevin Murphy, Yair Weiss, Michael I. Jordan
Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes.The most dr…
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Kevin Murphy
We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Ess…
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…