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11/04/2020
Causal Autoregressive Flows
Two apparently unrelated fields – normalizing flows and causality – have...
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09/25/2020
Towards the interpretation of time-varying regularization parameters in streaming penalized regression models
High-dimensional, streaming datasets are ubiquitous in modern applicatio...
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07/20/2020
Bayesian optimization for automatic design of face stimuli
Investigating the cognitive and neural mechanisms involved with face pro...
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07/18/2020
Autoregressive flow-based causal discovery and inference
We posit that autoregressive flow models are well-suited to performing a...
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02/26/2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models
Despite the growing popularity of energy-based models, their identifiabi...
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08/05/2019
Interpretable brain age prediction using linear latent variable models of functional connectivity
Neuroimaging-driven prediction of brain age, defined as the predicted bi...
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04/19/2019
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
We consider the problem of inferring causal relationships between two or...
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05/24/2018
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Connectivity estimation is challenging in the context of high-dimensiona...
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10/28/2016
Adaptive regularization for Lasso models in the context of non-stationary data streams
Large scale, streaming datasets are ubiquitous in modern machine learnin...
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05/01/2016
Text-mining the NeuroSynth corpus using Deep Boltzmann Machines
Large-scale automated meta-analysis of neuroimaging data has recently es...
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12/07/2015
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
In neuroimaging data analysis, Gaussian graphical models are often used ...
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11/06/2015
Streaming regularization parameter selection via stochastic gradient descent
We propose a framework to perform streaming covariance selection. Our ap...
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02/08/2015
