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Found 12 papers, 4 papers with code
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms.
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.
Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation
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.
Chronological Causal Bandits
This paper studies an instance of the multi-armed bandit (MAB) problem, specifically where several causal MABs operate chronologically in the same dynamical system.
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.
Prospective Artificial Intelligence Approaches for Active Cyber Defence
Cybercriminals are rapidly developing new malicious tools that leverage artificial intelligence (AI) to enable new classes of adaptive and stealthy attacks.
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.
Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network
There is urgent need for non-intrusive tests that can detect early signs of Parkinson{'}s disease (PD), a debilitating neurodegenerative disorder that affects motor control.
Boltzmann Exploration Expectation–Maximisation
While effective, the success of any monotone algorithm is crucially dependant on good parameter initialisation, where a common choice is K-means initialisation, commonly employed for Gaussian mixture models.
Boltzmann Exploration Expectation-Maximisation
While effective, the success of any monotone algorithm is crucially dependant on good parameter initialisation, where a common choice is $K$-means initialisation, commonly employed for Gaussian mixture models.
Coregionalised Locomotion Envelopes - A Qualitative Approach
'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing.
