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Simon O'Callaghan

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4 papers

1 author row

AAAI Conference 2016 Conference Paper

Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression

Ransalu Senanayake
Simon O'Callaghan
Fabio Ramos

Understanding and predicting how inﬂuenza propagates is vital to reduce its impact. In this paper we develop a nonparametric model based on Gaussian process (GP) regression to capture the complex spatial and temporal dependencies present in the data. A stochastic variational inference approach was adopted to address scalability. Rather than modeling the problem as a time-series as in many studies, we capture the space-time dependencies by combining different kernels. A kernel averaging technique which converts spatiallydiffused point processes to an area process is proposed to model geographical distribution. Additionally, to accurately model the variable behavior of the time-series, the GP kernel is further modiﬁed to account for non-stationarity and seasonality. Experimental results on two datasets of state-wide US weekly ﬂu-counts consisting of 19, 698 and 89, 474 data points, ranging over several years, illustrate the robustness of the model as a tool for further epidemiological investigations.

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NeurIPS Conference 2016 Conference Paper

Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments

Ransalu Senanayake
Lionel Ott
Simon O'Callaghan
Fabio Ramos

We consider the problem of building continuous occupancy representations in dynamic environments for robotics applications. The problem has hardly been discussed previously due to the complexity of patterns in urban environments, which have both spatial and temporal dependencies. We address the problem as learning a kernel classifier on an efficient feature space. The key novelty of our approach is the incorporation of variations in the time domain into the spatial domain. We propose a method to propagate motion uncertainty into the kernel using a hierarchical model. The main benefit of this approach is that it can directly predict the occupancy state of the map in the future from past observations, being a valuable tool for robot trajectory planning under uncertainty. Our approach preserves the main computational benefits of static Hilbert maps — using stochastic gradient descent for fast optimization of model parameters and incremental updates as new data are captured. Experiments conducted in road intersections of an urban environment demonstrated that spatio-temporal Hilbert maps can accurately model changes in the map while outperforming other techniques on various aspects.

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IJCAI Conference 2013 Conference Paper

Bayesian Joint Inversions for the Exploration of Earth Resources

Alistair Reid
Simon O'Callaghan
Edwin V. Bonilla
Lachlan McCalman
Tim Rawling
Fabio Ramos

We propose a machine learning approach to geophysical inversion problems for the exploration of earth resources. Our approach is based on nonparametric Bayesian methods, specifically, Gaussian processes, and provides a full distribution over the predicted geophysical properties whilst enabling the incorporation of data from different modalities. We assess our method both qualitatively and quantitatively using a real dataset from South Australia containing gravity and drill-hole data and through simulated experiments involving gravity, drill-holes and magnetics, with the goal of characterizing rock densities. The significance of our probabilistic inversion extends to general exploration problems with potential to dramatically benefit the industry.

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AAAI Conference 2011 Conference Paper

Continuous Occupancy Mapping with Integral Kernels

Simon O'Callaghan
Fabio Ramos

We address the problem of building a continuous occupancy representation of the environment with ranging sensors. Observations from such sensors provide two types of information: a line segment or a beam indicating no returns along them (free-space); a point or return at the end of the segment representing an occupied surface. To model these two types of observations in a principled statistical manner, we propose a novel methodology based on integral kernels. We show that integral kernels can be directly incorporated into a Gaussian process classiﬁcation (GPC) framework to provide a continuous non-parametric Bayesian estimation of occupancy. Directly handling line segment and point observations avoids the need to discretise segments into points, reducing the computational cost of GPC inference and learning. We present experiments on 2D and 3D datasets demonstrating the beneﬁts of the approach.

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