95 citations · 160 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…