Estimating time-varying brain connectivity networks from functional MRI time series. - Ricardo Pio Monti; Peter Hellyer; David J. Sharp; Robert Leech; Christoforos Anagnostopoulos; Giovanni Montana (NeuroImage, 2014)

Graph Embeddings of Dynamic Functional Connectivity Reveal Discriminative Patterns of Task Engagement in HCP Data. - Ricardo Pio Monti; Romy Lorenz; Peter Hellyer; Robert Leech; Christoforos Anagnostopoulos; Giovanni Montana (PRNI, 2015)

Classifying HCP task-fMRI networks using heat kernels. - Ai Wern Chung; Emanuele Pesce; Ricardo Pio Monti; Giovanni Montana (PRNI, 2016)

Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization. - Romy Lorenz; Ricardo Pio Monti; Adam Hampshire; Yury Koush; Christoforos Anagnostopoulos; Aldo A. Faisal; David J. Sharp; Giovanni Montana; Robert Leech; Inês R. Violante (PRNI, 2016)

Text-mining the neurosynth corpus using deep boltzmann machines. - Ricardo Pio Monti; Romy Lorenz; Robert Leech; Christoforos Anagnostopoulos; Giovanni Montana (PRNI, 2016)

A framework for adaptive regularization in streaming Lasso models. - Ricardo Pio Monti; Christoforos Anagnostopoulos; Giovanni Montana (CoRR, 2016)

The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI. - Romy Lorenz; Ricardo Pio Monti; Inês R. Violante; Christoforos Anagnostopoulos; Aldo A. Faisal; Giovanni Montana; Robert Leech (NeuroImage, 2016)

Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods. - Ricardo Pio Monti; Romy Lorenz; Peter Hellyer; Robert Leech; Christoforos Anagnostopoulos; Giovanni Montana (Frontiers Comput. Neurosci., 2017)

Avoiding Degradation in Deep Feed-Forward Networks by Phasing Out Skip-Connections. - Ricardo Pio Monti; Sina Tootoonian; Robin Cao (ICANN (3), 2018)

A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data . - Ricardo Pio Monti; Aapo Hyvärinen (UAI, 2018)

Adaptive regularization for Lasso models in the context of nonstationary data streams. - Ricardo Pio Monti; Christoforos Anagnostopoulos; Giovanni Montana (Stat. Anal. Data Min., 2018)

Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. - Ricardo Pio Monti; Kun Zhang 001; Aapo Hyvärinen (UAI, 2019)

Towards the interpretation of time-varying regularization parameters in streaming penalized regression models. - Lenka Zbonáková; Ricardo Pio Monti; Wolfgang Karl Härdle (Pattern Recognit. Lett., 2019)

Variational Autoencoders and Nonlinear ICA: A Unifying Framework. - Ilyes Khemakhem; Diederik P. Kingma; Ricardo Pio Monti; Aapo Hyvärinen (AISTATS, 2020)

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. - Ilyes Khemakhem; Ricardo Pio Monti; Diederik P. Kingma; Aapo Hyvärinen (NeurIPS, 2020)

Robust contrastive learning and nonlinear ICA in the presence of outliers. - Hiroaki Sasaki; Takashi Takenouchi; Ricardo Pio Monti; Aapo Hyvärinen (UAI, 2020)

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. - Ilyes Khemakhem; Ricardo Pio Monti; Diederik P. Kingma; Aapo Hyvärinen (CoRR, 2020)

Autoregressive flow-based causal discovery and inference. - Ricardo Pio Monti; Ilyes Khemakhem; Aapo Hyvärinen (CoRR, 2020)

Bayesian optimization for automatic design of face stimuli. - Pedro F. Da Costa; Romy Lorenz; Ricardo Pio Monti; Emily J. H. Jones; Robert Leech (CoRR, 2020)

Causal Autoregressive Flows. - Ilyes Khemakhem; Ricardo Pio Monti; Robert Leech; Aapo Hyvärinen (AISTATS, 2021)

Controllable Generative Modeling via Causal Reasoning. - Joey Bose; Ricardo Pio Monti; Aditya Grover (Trans. Mach. Learn. Res., 2022)

Identifiability of latent-variable and structural-equation models: from linear to nonlinear. - Aapo Hyvärinen; Ilyes Khemakhem; Ricardo Pio Monti (CoRR, 2023)