A Minimal Learning Machine for Datasets with Missing Values. - Diego Parente Paiva Mesquita; João Paulo Pordeus Gomes; Amauri H. Souza Jr. (ICONIP (1), 2015)

Ensemble of Minimal Learning Machines for Pattern Classification. - Diego Parente Paiva Mesquita; João Paulo Pordeus Gomes; Amauri Holanda Souza Júnior (IWANN (2), 2015)

Co-MLM: A SSL Algorithm Based on the Minimal Learning Machine. - Weslley L. Caldas; João Paulo Pordeus Gomes; Michelle G. Cacais; Diego P. P. Mesquita (BRACIS, 2016)

Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator. - Filipe F. R. Damasceno; Marcelo B. A. Veras; Diego P. P. Mesquita; João Paulo Pordeus Gomes; Carlos Eduardo Fisch de Brito (BRACIS, 2016)

Extreme Learning Machines for Datasets with Missing Values Using the Unscented Transform. - Diego P. P. Mesquita; João Paulo Pordeus Gomes; Leonardo Ramos Rodrigues (BRACIS, 2016)

K-means for Datasets with Missing Attributes: Building Soft Constraints with Observed and Imputed Values. - Diego P. P. Mesquita; João P. P. Gomes; Leonardo Ramos Rodrigues (ESANN, 2016)

Using Robust Extreme Learning Machines to Predict Cotton Yarn Strength and Hairiness. - Diego P. P. Mesquita; Antônio C. Araújo Neto; Jose Queiroz Neto; João P. P. Gomes; Leonardo Ramos Rodrigues (ESANN, 2016)

Radial Basis Function Neural Networks for Datasets with Missing Values. - Diego Parente Paiva Mesquita; João Paulo Pordeus Gomes (ISDA, 2016)

Classification with reject option for software defect prediction. - Diego P. P. Mesquita; Lincoln S. Rocha; João P. P. Gomes; Ajalmar R. da Rocha Neto (Appl. Soft Comput., 2016)

A Robust Minimal Learning Machine based on the M-Estimator. - João P. P. Gomes; Diego P. P. Mesquita; Ananda Freire; Amauri H. Souza Júnior; Tommi Kärkkäinen (ESANN, 2017)

Forward Stagewise Regression on Incomplete Datasets. - Marcelo B. A. Veras; Diego P. P. Mesquita; João P. P. Gomes; Amauri H. Souza Júnior; Guilherme A. Barreto (IWANN (1), 2017)

Euclidean distance estimation in incomplete datasets. - Diego P. P. Mesquita; João P. P. Gomes; Amauri Holanda Souza Júnior; Juvêncio Santos Nobre (Neurocomputing, 2017)

Ensemble of Efficient Minimal Learning Machines for Classification and Regression. - Diego P. P. Mesquita; João P. P. Gomes; Amauri Holanda de Souza Júnior (Neural Process. Lett., 2017)

Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm. - Diego P. P. Mesquita; João Paulo Pordeus Gomes; Leonardo Ramos Rodrigues; Saulo A. F. Oliveira; Roberto Kawakami Harrop Galvão (Appl. Soft Comput., 2018)

Fast Co-MLM: An Efficient Semi-supervised Co-training Method Based on the Minimal Learning Machine. - Weslley L. Caldas; João P. P. Gomes; Diego P. P. Mesquita (New Gener. Comput., 2018)

Embarrassingly Parallel MCMC using Deep Invertible Transformations. - Diego Mesquita; Paul Blomstedt; Samuel Kaski (UAI, 2019)

Gaussian kernels for incomplete data. - Diego P. P. Mesquita; João P. P. Gomes; Francesco Corona; Amauri Holanda de Souza Júnior; Juvêncio S. Nobre (Appl. Soft Comput., 2019)

Artificial Neural Networks with Random Weights for Incomplete Datasets. - Diego P. P. Mesquita; João Paulo Pordeus Gomes; Leonardo Ramos Rodrigues (Neural Process. Lett., 2019)

Rethinking pooling in graph neural networks. - Diego Mesquita; Amauri H. Souza Jr.; Samuel Kaski (NeurIPS, 2020)

Variance reduction for distributed stochastic gradient MCMC. - Khaoula el Mekkaoui; Diego Mesquita; Paul Blomstedt; Samuel Kaski (CoRR, 2020)

A sparse linear regression model for incomplete datasets. - Marcelo B. A. Veras; Diego P. P. Mesquita; César Lincoln C. Mattos; João P. P. Gomes (Pattern Anal. Appl., 2020)

LS-SVR as a Bayesian RBF Network. - Diego P. P. Mesquita; Luis A. Freitas; João P. P. Gomes; César L. C. Mattos (IEEE Trans. Neural Networks Learn. Syst., 2020)

Learning GPLVM with arbitrary kernels using the unscented transformation. - Daniel Augusto de Souza; Diego Mesquita; João Paulo Pordeus Gomes; César Lincoln C. Mattos (AISTATS, 2021)

Improving Graph Variational Autoencoders with Multi-Hop Simple Convolutions. - Erik Jhones F. do Nascimento; Amauri H. Souza; Diego Mesquita (ESANN, 2021)

Federated stochastic gradient Langevin dynamics. - Khaoula el Mekkaoui; Diego Mesquita; Paul Blomstedt; Samuel Kaski (UAI, 2021)

Advances in distributed Bayesian inference and graph neural networks. - Diego Mesquita (2021)

Parallel MCMC Without Embarrassing Failures. - Daniel Augusto de Souza; Diego Mesquita; Samuel Kaski; Luigi Acerbi (AISTATS, 2022)

ConveXplainer for Graph Neural Networks. - Tamara A. Pereira; Erik Jhones F. do Nascimento; Diego Mesquita; Amauri H. Souza (BRACIS (2), 2022)

Bayesian Analysis of Bug-Fixing Time using Report Data. - Renan Gomes Vieira; Diego Mesquita; César Lincoln C. Mattos; Ricardo Britto 001; Lincoln S. Rocha; João Gomes 002 (ESEM, 2022)

Provably expressive temporal graph networks. - Amauri H. Souza; Diego Mesquita; Samuel Kaski; Vikas Garg 001 (NeurIPS, 2022)

Bayesian Multilateration. - Alisson S. C. Alencar; César L. C. Mattos; João P. P. Gomes; Diego Mesquita (IEEE Signal Process. Lett., 2022)

Distill n' Explain: explaining graph neural networks using simple surrogates. - Tamara A. Pereira; Erik Nascimento; Lucas E. Resck; Diego Mesquita; Amauri H. Souza (AISTATS, 2023)

Thin and deep Gaussian processes. - Daniel Augusto de Souza; Alexander Nikitin; St John; Magnus Ross; Mauricio A. Álvarez; Marc Peter Deisenroth; João Paulo Pordeus Gomes; Diego Mesquita; César Lincoln C. Mattos (NeurIPS, 2023)

Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition. - Yuling Yao; Luiz Max Carvalho; Diego Mesquita; Yann McLatchie (CoRR, 2023)

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets. - Tiago da Silva; Eliezer S. Silva; Adèle H. Ribeiro; António Góis; Dominik Heider; Samuel Kaski; Diego Mesquita (CoRR, 2023)

A Contrastive Objective for Training Continuous Generative Flow Networks. - Tiago da Silva; Diego Mesquita (BRACIS (1), 2024)

Amortized Variational Deep Kernel Learning. - Alan L. S. Matias; César Lincoln C. Mattos; João Paulo Pordeus Gomes; Diego Mesquita (ICML, 2024)

Embarrassingly Parallel GFlowNets. - Tiago da Silva; Luiz Max Carvalho; Amauri H. Souza; Samuel Kaski; Diego Mesquita (ICML, 2024)

Streaming Bayes GFlowNets. - Tiago da Silva; Daniel Augusto de Souza; Diego Mesquita (NeurIPS, 2024)

On Divergence Measures for Training GFlowNets. - Tiago da Silva; Eliezer de Souza da Silva; Diego Mesquita (NeurIPS, 2024)

Bayesian ART for incomplete datasets. - Alan L. S. Matias; João Paulo Pordeus Gomes; César Lincoln C. Mattos; Ajalmar R. da Rocha Neto; Diego Mesquita (Appl. Soft Comput., 2024)

In-n-Out: Calibrating Graph Neural Networks for Link Prediction. - Erik Nascimento; Diego Mesquita; Samuel Kaski; Amauri H. Souza (CoRR, 2024)

Towards automatic labeling of exception handling bugs: A case study of 10 years bug-fixing in Apache Hadoop. - Antônio da Silva; Renan Gomes Vieira; Diego P. P. Mesquita; João Paulo Pordeus Gomes; Lincoln S. Rocha (Empir. Softw. Eng., 2024)

When do GFlowNets learn the right distribution? - Tiago da Silva; Rodrigo Barreto Alves; Eliezer de Souza da Silva; Amauri H. Souza; Vikas Garg 001; Samuel Kaski; Diego Mesquita (ICLR, 2025)

Generalization and Distributed Learning of GFlowNets. - Tiago Silva; Amauri H. Souza; Omar Rivasplata; Vikas Garg 001; Samuel Kaski; Diego Mesquita (ICLR, 2025)

Differentially Private Selection Using Smooth Sensitivity. - Iago C. Chaves; Victor A. E. de Farias; Amanda Perez; Diego Mesquita; Javam C. Machado (SP, 2025)

Cooperative Sheaf Neural Networks. - André Ribeiro; Ana Luiza Tenorio; Juan Belieni; Amauri H. Souza; Diego Mesquita (CoRR, 2025)