activity
20122019
most citedA Framework for Evaluating Approximation Methods for Gaussian Process Regression

68 citations · 77 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20193 cited

Customizing Sequence Generation with Multi-Task Dynamical Systems

Alex Bird, Christopher K. I. Williams

Dynamical system models (including RNNs) often lack the ability to adapt the sequence generation or prediction to a given context, limiting their real-world application. In this pa…

cs.LG20191 cited

Multi-Task Time Series Analysis applied to Drug Response Modelling

Alex Bird, Christopher K. I. Williams, Christopher Hawthorne

Time series models such as dynamical systems are frequently fitted to a cohort of data, ignoring variation between individual entities such as patients. In this paper we show how t…

stat.ML20161 cited

Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers

Adam McCarthy, Christopher K. I. Williams

Bedside monitors in Intensive Care Units (ICUs) frequently sound incorrectly, slowing response times and desensitising nurses to alarms (Chambrin, 2001), causing true alarms to be…

cs.LG20163 cited

Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring

Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne

We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are…

cs.AI20141 cited

Renewal Strings for Cleaning Astronomical Databases

Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams +1

Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious records coming from artefactual e…

stat.ML201268 cited

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray

Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine learning. However, even a straightforward implementation of Gaussian process regr…