625 citations · 2.7k across the 69 of their papers we have counts for
5 papers · 2 filters
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil Lawrence
In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike sever…
Variational Gaussian Process Dynamical Systems
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences)…
Linear Latent Force Models using Gaussian Processes
Mauricio A. Álvarez, David Luengo, Neil D. Lawrence
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On t…
Residual Component Analysis
Alfredo A. Kalaitzis, Neil D. Lawrence
Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise, Sigma = (sigma^2)*I.…
Kernels for Vector-Valued Functions: a Review
Mauricio A. Alvarez, Lorenzo Rosasco, Neil D. Lawrence
Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they pr…