95 citations · 160 across the 14 of their papers we have counts for
16 papers
Notes on the H-measure of classifier performance
D. J. Hand, C. Anagnostopoulos
The H-measure is a classifier performance measure which takes into account the context of application without requiring a rigid value of relative misclassification costs to be set.…
Adaptive regularization for Lasso models in the context of non-stationary data streams
Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana
Large scale, streaming datasets are ubiquitous in modern machine learning. Streaming algorithms must be scalable, amenable to incremental training and robust to the presence of non…
Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization
Romy Lorenz, Ricardo Pio Monti, Adam Hampshire +7
Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex co…
Text-mining the NeuroSynth corpus using Deep Boltzmann Machines
Ricardo Pio Monti, Romy Lorenz, Robert Leech +2
Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function. This research…
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Ty…
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Romy Lorenz, Ricardo P Monti, Ines R Violante +4
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined…
Streaming regularization parameter selection via stochastic gradient descent
Ricardo Pio Monti, Romy Lorenz, Robert Leech +2
We propose a framework to perform streaming covariance selection. Our approach employs regularization constraints where a time-varying sparsity parameter is iteratively estimated v…
Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data
Ricardo Pio Monti, Romy Lorenz, Peter Hellyer +3
There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estima…
The Automatic Neuroscientist: automated experimental design with real-time fMRI
Romy Lorenz, Ricardo Pio Monti, Ines R. Violante +4
A standard approach in functional neuroimaging explores how a particular cognitive task activates a set of brain regions (one task-to-many regions mapping). Importantly though, the…
Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces
Ricardo Pio Monti, Romy Lorenz, Christoforos Anagnostopoulos +2
There has been an explosion of interest in functional Magnetic Resonance Imaging (MRI) during the past two decades. Naturally, this has been accompanied by many major advances in t…
Estimating Optimal Active Learning via Model Retraining Improvement
Lewis P. G. Evans, Niall M. Adams, Christoforos Anagnostopoulos
A central question for active learning (AL) is: "what is the optimal selection?" Defining optimality by classifier loss produces a new characterisation of optimal AL behaviour, by…
When does Active Learning Work?
Lewis Evans, Niall M. Adams, Christoforos Anagnostopoulos
Active Learning (AL) methods seek to improve classifier performance when labels are expensive or scarce. We consider two central questions: Where does AL work? How much does it hel…
Targeting Optimal Active Learning via Example Quality
Lewis P. G. Evans, Niall M. Adams, Christoforos Anagnostopoulos
In many classification problems unlabelled data is abundant and a subset can be chosen for labelling. This defines the context of active learning (AL), where methods systematically…
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series
Ricardo Pio Monti, Peter Hellyer, David Sharp +3
Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be station…
A better Beta for the H measure of classification performance
David J. Hand, Christoforos Anagnostopoulos
The area under the ROC curve is widely used as a measure of performance of classification rules. However, it has recently been shown that the measure is fundamentally incoherent, i…
Dynamic trees for streaming and massive data contexts
Christoforos Anagnostopoulos, Robert B. Gramacy
Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time information. Learning algorithms deploye…