24 citations · 52 across the 10 of their papers we have counts for
10 papers
Causal Entropy Optimization
Nicola Branchini, Virginia Aglietti, Neil Dhir +1
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. This problem arises in many a…
Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation
Alex Andrew, Sam Spillard, Joshua Collyer +1
In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particul…
Chronological Causal Bandits
Neil Dhir
This paper studies an instance of the multi-armed bandit (MAB) problem, specifically where several causal MABs operate chronologically in the same dynamical system. Practically the…
Dynamic Causal Bayesian Optimization
Virginia Aglietti, Neil Dhir, Javier González +1
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…
Prospective Artificial Intelligence Approaches for Active Cyber Defence
Neil Dhir, Henrique Hoeltgebaum, Niall Adams +3
Cybercriminals are rapidly developing new malicious tools that leverage artificial intelligence (AI) to enable new classes of adaptive and stealthy attacks. New defensive methods n…
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
Chance Haycock, Edward Thorpe-Woods, James Walsh +4
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…
Near Real-Time Social Distance Estimation in London
James Walsh, Oluwafunmilola Kesa, Andrew Wang +6
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. Large we…
Boltzmann Exploration Expectation-Maximisation
Mathias Edman, Neil Dhir
We present a general method for fitting finite mixture models (FMM). Learning in a mixture model consists of finding the most likely cluster assignment for each data-point, as well…
Generalising Cost-Optimal Particle Filtering
Andrew Warrington, Neil Dhir
We present an instance of the optimal sensor scheduling problem with the additional relaxation that our observer makes active choices whether or not to observe and how to observe.…
Coregionalised Locomotion Envelopes - A Qualitative Approach
Neil Dhir, Houman Dallali, Mo Rastgaar
'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be…