No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization. - Thiago de P. Vasconcelos; Daniel Augusto R. M. A. de Souza; César L. C. Mattos; João P. P. Gomes (ICTAI, 2019)

Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels. - Daniel Augusto R. M. A. de Souza; César Lincoln C. Mattos; João Paulo Pordeus Gomes (CoRR, 2019)

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)

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

Self-tuning portfolio-based Bayesian optimization. - Thiago de P. Vasconcelos; Daniel Augusto R. M. A. de Souza; Gustavo C. de M. Virgolino; César L. C. Mattos; João P. P. Gomes (Expert Syst. Appl., 2022)

Actually Sparse Variational Gaussian Processes. - Harry Jake Cunningham; Daniel Augusto de Souza; So Takao; Mark van der Wilk; Marc Peter Deisenroth (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)

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

When Can Proxies Improve the Sample Complexity of Preference Learning? - Yuchen Zhu; Daniel Augusto de Souza; Zhengyan Shi; Mengyue Yang; Pasquale Minervini; Alexander D'Amour; Matt J. Kusner (CoRR, 2024)