TimeMesh - A Serious Game for European Citizenship. - Ricardo Baptista; Carlos Vaz de Carvalho (EAI Endorsed Trans. Serious Games, 2013)

Training and Support system in the Cloud for improving the situational awareness in Search and Rescue (SAR) operations. - Michal Pelka; Karol Majek; Janusz Bedkowski; Pawel Musialik; Andrzej Maslowski; Geert De Cubber; Haris Balta; António Coelho 001; Ricardo Gonçalves 005; Ricardo Baptista; Jose M. Sanchez; Shashank Govindaraj (SSRR, 2014)

Relation Between Game Genres and Competences for In-Game Certification. - Ricardo Baptista; António Coelho 001; Carlos Vaz de Carvalho (SGAMES, 2015)

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting. - Rebecca E. Morrison; Ricardo Baptista; Youssef M. Marzouk (NIPS, 2017)

Bayesian Optimization of Combinatorial Structures. - Ricardo Baptista; Matthias Poloczek (ICML, 2018)

Estimation of Vineyard Productivity Map Considering a Cost-Effective LIDAR-Based Sensor. - Pedro Moura; Daniela Ribeiro; Filipe Neves dos Santos; Alberto Gomes; Ricardo Baptista; Mario Cunha (EPIA (1), 2019)

Some greedy algorithms for sparse polynomial chaos expansions. - Ricardo Baptista; Valentin Stolbunov; Prasanth B. Nair (J. Comput. Phys., 2019)

Conditional Sampling With Monotone GANs. - Nikola B. Kovachki; Ricardo Baptista; Bamdad Hosseini; Youssef M. Marzouk (CoRR, 2020)

An adaptive transport framework for joint and conditional density estimation. - Ricardo Baptista; Olivier Zahm; Youssef M. Marzouk (CoRR, 2020)

Diagonal nonlinear transformations preserve structure in covariance and precision matrices. - Rebecca E. Morrison; Ricardo Baptista; Estelle L. Basor (J. Multivar. Anal., 2022)

MParT: Monotone Parameterization Toolkit. - Matthew D. Parno; Paul-Baptiste Rubio; Daniel Sharp; Michael Brennan; Ricardo Baptista; Henning Bonart; Youssef M. Marzouk (J. Open Source Softw., 2022)

Structured Neural Networks for Density Estimation and Causal Inference. - Asic Q. Chen; Ruian Shi; Xiang Gao 019; Ricardo Baptista; Rahul G. Krishnan (NeurIPS, 2023)

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. - Zhong Yi Wan; Ricardo Baptista; Anudhyan Boral; Yi-Fan Chen; John Anderson; Fei Sha; Leonardo Zepeda-Núñez (NeurIPS, 2023)

Score-based Diffusion Models in Function Space. - Jae Hyun Lim 001; Nikola B. Kovachki; Ricardo Baptista; Christopher Beckham; Kamyar Azizzadenesheli; Jean Kossaifi; Vikram Voleti; Jiaming Song; Karsten Kreis; Jan Kautz; Christopher Pal; Arash Vahdat; et al. (CoRR, 2023)

A generative flow for conditional sampling via optimal transport. - Jason Alfonso; Ricardo Baptista; Anupam Bhakta; Noam Gal; Alfin Hou; Isa Lyubimova; Daniel Pocklington; Josef Sajonz; Giulio Trigila; Ryan Tsai (CoRR, 2023)

Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots. - Théo Bourdais; Pau Batlle; Xianjin Yang; Ricardo Baptista; Nicolas Rouquette; Houman Owhadi (CoRR, 2023)

Computational Optimal Transport and Filtering on Riemannian Manifolds. - Daniel Grange; Mohammad Al-Jarrah; Ricardo Baptista; Amirhossein Taghvaei; Tryphon T. Georgiou; Sean Phillips; Allen R. Tannenbaum (IEEE Control. Syst. Lett., 2023)

Ensemble transport smoothing. Part II: Nonlinear updates. - Maximilian Ramgraber; Ricardo Baptista; Dennis McLaughlin; Youssef M. Marzouk (J. Comput. Phys. X, 2023)

Ensemble transport smoothing. Part I: Unified framework. - Maximilian Ramgraber; Ricardo Baptista; Dennis McLaughlin; Youssef M. Marzouk (J. Comput. Phys. X, 2023)

Distributed Nonlinear Filtering using Triangular Transport Maps. - Daniel Grange; Ricardo Baptista; Amirhossein Taghvaei; Allen R. Tannenbaum; Sean Phillips (ACC, 2024)

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis. - Qiao Chen; Elise Arnaud; Ricardo Baptista; Olivier Zahm (CoRR, 2024)

Learning Optimal Filters Using Variational Inference. - Enoch Luk; Eviatar Bach; Ricardo Baptista; Andrew M. Stuart (CoRR, 2024)

Inverse Problems and Data Assimilation: A Machine Learning Approach. - Eviatar Bach; Ricardo Baptista; Daniel Sanz-Alonso; Andrew M. Stuart (CoRR, 2024)

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport. - Ricardo Baptista; Lianghao Cao; Joshua Chen; Omar Ghattas; Fengyi Li; Youssef M. Marzouk; J. Tinsley Oden (J. Comput. Phys., 2024)

Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport. - Ricardo Baptista; Rebecca E. Morrison; Olivier Zahm; Youssef M. Marzouk (J. Mach. Learn. Res., 2024)

Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference. - Ricardo Baptista; Bamdad Hosseini; Nikola B. Kovachki; Youssef M. Marzouk (SIAM/ASA J. Uncertain. Quantification, 2024)

An approximation theory framework for measure-transport sampling algorithms. - Ricardo Baptista; Bamdad Hosseini; Nikola B. Kovachki; Youssef Marzouk 001; Amir Sagiv (Math. Comput., 2024)

Training and Certification of Competences through Serious Games. - Ricardo Baptista; António Coelho 001; Carlos Vaz de Carvalho (Comput., 2024)

On the Representation and Learning of Monotone Triangular Transport Maps. - Ricardo Baptista; Youssef M. Marzouk; Olivier Zahm (Found. Comput. Math., 2024)

Learning Local Neighborhoods of Non-Gaussian Graphical Models. - Sarah Liaw; Rebecca E. Morrison; Youssef M. Marzouk; Ricardo Baptista (AAAI, 2025)

Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps. - Ricardo Baptista; Aram-Alexandre Pooladian; Michael Brennan; Youssef Marzouk 001; Jonathan Niles-Weed (AISTATS, 2025)

Neural Approximate Mirror Maps for Constrained Diffusion Models. - Berthy Feng; Ricardo Baptista; Katherine L. Bouman (ICLR, 2025)

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference. - Zheyu Oliver Wang; Ricardo Baptista; Youssef Marzouk 001; Lars Ruthotto; Deepanshu Verma (SIAM J. Sci. Comput., 2025)

Dimension reduction via score ratio matching. - Ricardo Baptista; Michael Brennan; Youssef Marzouk 001 (Trans. Mach. Learn. Res., 2025)

Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems. - Hongkai Zheng; Wenda Chu; Austin Wang; Nikola Borislavov Kovachki; Ricardo Baptista; Yisong Yue (Trans. Mach. Learn. Res., 2025)

Memorization and Regularization in Generative Diffusion Models. - Ricardo Baptista; Agnimitra Dasgupta; Nikola B. Kovachki; Assad A. Oberai; Andrew M. Stuart (CoRR, 2025)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks. - Ricardo Baptista; Edoardo Calvello; Matthieu Darcy; Houman Owhadi; Andrew M. Stuart; Xianjin Yang (CoRR, 2025)

Learning local neighborhoods of non-Gaussian graphical models: A measure transport approach. - Sarah Liaw; Rebecca E. Morrison; Youssef Marzouk 001; Ricardo Baptista (CoRR, 2025)

Learning Enhanced Ensemble Filters. - Eviatar Bach; Ricardo Baptista; Edoardo Calvello; Bohan Chen; Andrew M. Stuart (CoRR, 2025)

A Mathematical Perspective On Contrastive Learning. - Ricardo Baptista; Andrew M. Stuart; Son Tran (CoRR, 2025)