95 citations · 234 across the 18 of their papers we have counts for
21 papers
Identifiability of latent-variable and structural-equation models: from linear to nonlinear
Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Monti
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i.e. some parameters cannot be uniquely estimated. In factor (component) analysis…
Causal Autoregressive Flows
Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech +1
Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community. In this work, we highlight an…
Towards the interpretation of time-varying regularization parameters in streaming penalized regression models
Lenka Zboňáková, Ricardo Pio Monti, Wolfgang Karl Härdle
High-dimensional, streaming datasets are ubiquitous in modern applications. Examples range from finance and e-commerce to the study of biomedical and neuroimaging data. As a result…
Autoregressive flow-based causal discovery and inference
Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvarinen
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual…
Bayesian optimization for automatic design of face stimuli
Pedro F. da Costa, Romy Lorenz, Ricardo Pio Monti +2
Investigating the cognitive and neural mechanisms involved with face processing is a fundamental task in modern neuroscience and psychology. To date, the majority of such studies h…
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma +1
We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional ene…
Robust contrastive learning and nonlinear ICA in the presence of outliers
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti +1
Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practic…
Interpretable brain age prediction using linear latent variable models of functional connectivity
Ricardo Pio Monti, Alex Gibberd, Sandipan Roy +6
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we…
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen
We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied…
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Ricardo Pio Monti, Aapo Hyvärinen
Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how…
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…
Classifying HCP Task-fMRI Networks Using Heat Kernels
Ai Wern Chung, Emanuele Pesce, Ricardo Pio Monti +1
Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent ad…
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 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…