Search Results for author:
Found 40 papers, 19 papers with code
Non-separable Non-stationary random fields
We describe a framework for constructing non-separable non-stationary random fields that is based on an infinite mixture of convolved stochastic processes.
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Decision making under uncertainty is challenging as the data-generating process (DGP) is often unknown.
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology.
Physics-Informed Variational State-Space Gaussian Processes
Differential equations are important mechanistic models that are integral to many scientific and engineering applications.
Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Decision making under uncertainty is challenging since the data-generating process (DGP) is often unknown.
Causally Abstracted Multi-armed Bandits
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems.
Interventionally Consistent Surrogates for Agent-based Simulators
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents.
Causal Optimal Transport of Abstractions
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them.
Robust Bayesian Inference for Berkson and Classical Measurement Error Models
This approach gives rise to a general framework that is suitable for both Classical and Berkson error models via the appropriate specification of the prior centering measure of a Dirichlet Process (DP).
Quantifying Consistency and Information Loss for Causal Abstraction Learning
However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models.
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution.
Causal Entropy Optimization
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed.
Towards Computing an Optimal Abstraction for Structural Causal Models
Working with causal models at different levels of abstraction is an important feature of science.
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible.
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results.
Spatio-Temporal Variational Gaussian Processes
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales linearly with respect to time.
Dynamic Causal Bayesian Optimization
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time.
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Stochastic processes are random variables with values in some space of paths.
A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities
We demonstrate how BSIM outperforms competing approaches on this large dataset in terms of prediction performances while providing results that are both interpretable and consistent with related indicators observed for the London region.
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention.
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
One of the Greater London Authority's (GLA) response to the COVID-19 pandemic brings together multiple large-scale and heterogeneous datasets capturing mobility, transportation and traffic activity over the city of London to better understand 'busyness' and enable targeted interventions and effective policy-making.
Near Real-Time Social Distance Estimation in London
During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources.
Generalised Bayesian Filtering via Sequential Monte Carlo
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.
Transforming Gaussian Processes With Normalizing Flows
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors.
Multi-task Causal Learning with Gaussian Processes
This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model.
Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
Over 30% of the ~4000 known exoplanets to date have been discovered using 'validation', where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated.
Variational Autoencoding of PDE Inverse Problems
Specifying a governing physical model in the presence of missing physics and recovering its parameters are two intertwined and fundamental problems in science.
Distribution Regression for Sequential Data
In this paper, we develop a rigorous mathematical framework for distribution regression where inputs are complex data streams.
Generalized Bayesian Filtering via Sequential Monte Carlo
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.
Probabilistic sequential matrix factorization
In particular, we consider nonlinear Gaussian state-space models where sequential approximate inference results in the factorization of a data matrix into a dictionary and time-varying coefficients with potentially nonlinear Markovian dependencies.
Multivariate Time Series Forecasting
Multivariate Time Series Imputation
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Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
In this paper, we are concerned with a non-asymptotic analysis of sampling algorithms used in nonconvex optimization.
Multi-resolution Multi-task Gaussian Processes
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels.
On the Constrained Least-cost Tour Problem
CLT is related to the family of Travelling Salesman Problems with Profits, but differs by defining the weight function on edges instead of vertices, and by requiring the total weight to be within a range instead of being at least some quota.
Structured Variational Inference in Continuous Cox Process Models
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function.
Non-linear Multitask Learning with Deep Gaussian Processes
We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes.
Generalized Variational Inference: Three arguments for deriving new Posteriors
We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences
The resulting inference procedure is doubly robust for both the predictive and the changepoint (CP) posterior, with linear time and constant space complexity.
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$-Divergences
The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity.
Efficient Inference in Multi-task Cox Process Models
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly.
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes.
