Ricardo Baptista

Orcid: 0000-0002-0317-6381

According to our database1, Ricardo Baptista authored at least 30 papers between 2013 and 2024.

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Bibliography

2024
Training and Certification of Competences through Serious Games.
Comput., August, 2024

Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference.
SIAM/ASA J. Uncertain. Quantification, 2024

Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport.
J. Mach. Learn. Res., 2024

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport.
J. Comput. Phys., 2024

Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems.
CoRR, 2024

Learning Optimal Filters Using Variational Inference.
CoRR, 2024

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis.
CoRR, 2024

Neural Approximate Mirror Maps for Constrained Diffusion Models.
CoRR, 2024

2023
Ensemble transport smoothing. Part I: Unified framework.
J. Comput. Phys. X, November, 2023

Ensemble transport smoothing. Part II: Nonlinear updates.
J. Comput. Phys. X, November, 2023

Computational Optimal Transport and Filtering on Riemannian Manifolds.
IEEE Control. Syst. Lett., 2023

Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots.
CoRR, 2023

Distributed Nonlinear Filtering using Triangular Transport Maps.
CoRR, 2023

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference.
CoRR, 2023

A generative flow for conditional sampling via optimal transport.
CoRR, 2023

An Approximation Theory Framework for Measure-Transport Sampling Algorithms.
CoRR, 2023

Score-based Diffusion Models in Function Space.
CoRR, 2023

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Structured Neural Networks for Density Estimation and Causal Inference.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
MParT: Monotone Parameterization Toolkit.
J. Open Source Softw., December, 2022

Diagonal nonlinear transformations preserve structure in covariance and precision matrices.
J. Multivar. Anal., 2022

2020
An adaptive transport framework for joint and conditional density estimation.
CoRR, 2020

Conditional Sampling With Monotone GANs.
CoRR, 2020

2019
Some greedy algorithms for sparse polynomial chaos expansions.
J. Comput. Phys., 2019

Estimation of Vineyard Productivity Map Considering a Cost-Effective LIDAR-Based Sensor.
Proceedings of the Progress in Artificial Intelligence, 2019

2018
Bayesian Optimization of Combinatorial Structures.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2015
Relation Between Game Genres and Competences for In-Game Certification.
Proceedings of the Serious Games, Interaction, and Simulation, 2015

2014
Training and Support system in the Cloud for improving the situational awareness in Search and Rescue (SAR) operations.
Proceedings of the 2014 IEEE International Symposium on Safety, 2014

2013
TimeMesh - A Serious Game for European Citizenship.
EAI Endorsed Trans. Serious Games, 2013


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