86 citations · 471 across the 33 of their papers we have counts for
7 papers · 1 filter
GPIRT: A Gaussian Process Model for Item Response Theory
JBrandon Duck-Mayr, Roman Garnett, Jacob M. Montgomery
The goal of item response theoretic (IRT) models is to provide estimates of latent traits from binary observed indicators and at the same time to learn the item response functions…
Active Search for Sparse Signals with Region Sensing
Yifei Ma, Roman Garnett, Jeff Schneider
Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress call…
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes
Steven Reece, Roman Garnett, Michael Osborne +1
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault fr…
Differentially Private Bayesian Optimization
Matt J. Kusner, Jacob R. Gardner, Roman Garnett +1
Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in…
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter, Michael A. Osborne, Roman Garnett +2
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Ca…
Propagation Kernels
Marion Neumann, Roman Garnett, Christian Bauckhage +1
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how info…