625 citations · 2.7k across the 69 of their papers we have counts for
7 papers · 1 filter
The Emergence of Organizing Structure in Conceptual Representation
Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum
Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalize…
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
Mu Niu, Zhenwen Dai, Neil Lawrence +1
Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to descr…
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
Alexander Grigorievskiy, Neil Lawrence, Simo Särkkä
We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed…
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek +1
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with…
Differentially Private Gaussian Processes
Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence
A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the di…
Chained Gaussian Processes
Alan D. Saul, James Hensman, Aki Vehtari +1
Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner o…