Ricardo Baptista | Ricardo Baptista

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Learning Probabilistic Graphical Models of Non-Gaussian Scientific Data
Ensemble transport smoothing--Part 1: unified framework
Ensemble transport smoothing--Part 2: nonlinear updates
Gradient-based dimension reduction for solving Bayesian inverse problems
Towards high-dimensional sequential inference
Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective
On the representation and learning of monotone triangular transport maps
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport
Representation and optimization of triangular transports
A low-rank ensemble Kalman filter for elliptic observations
Diagonal Nonlinear Transformations Preserve Structure in Covariance and Precision Matrices
Low-Dimensional Structure in Bayesian Inference Problems with Mixture Models
Sequential Bayesian inference via structured nonlinear couplings
Ensemble-based data assimilation via nonlinear couplings
Likelihood-free Bayesian inference via couplings
Learning non-Gaussian graphical models via Hessian scores and triangular transport
A low-rank nonlinear ensemble filter for vortex models of aerodynamic flows
Learning non-Gaussian graphical models
Conditional Sampling With Monotone GANs
High-dimensional ensemble filtering with nonlinear local couplings
Coupling techniques for nonlinear ensemble filtering
Some greedy algorithms for sparse polynomial chaos expansions
Bayesian Optimization of Combinatorial Structures
Optimal approximations of coupling in multidisciplinary models
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting