Selected publications
- I. Chaves, V. Farias, A. Perez, D. Mesquita, J. Machado. Differentially Private Selection using Smooth Sensitivity IEEE S&P 2025
- T. da Silva, A. Souza, O. Rivasplata, V. Garg, S. Kaski, D. Mesquita. Generalization and Distributed Learning of GFlowNets ICLR 2025
- T. da Silva, R. Alves, E. da Silva, A. Souza, V. Garg, S. Kaski, D. Mesquita. When do GFlowNets learn the right distribution? ICLR 2025
- T. da Silva, D. de Souza, D. Mesquita. Streaming Bayes GFlowNets NeurIPS 2024
- T. da Silva, E. Silva, D. Mesquita. On Divergence Measures for Training GFlowNets NeurIPS 2024
- P. Blomstedt, D. Mesquita, O. Rivasplata, J. Lintusaari, T. Sivula. J. Corander, S. Kaski. Meta-analysis of Bayesian analyses Bayesian Analysis 2024
- A. Matias, C. Mattos, J. Gomes, D. Mesquita. Amortized Variational Deep Kernel Learning ICML 2024
- T. da Silva, A. Souza, L. Carvalho, S. Kaski, D. Mesquita. Embarrassingly Parallel GFlowNets ICML 2024
- D. de Souza, A. Nikitin, S. T. John, M. Ross, M. A. Álvarez, M. P. Deisenroth, J. Gomes, D. Mesquita, C. Mattos. Thin and deep Gaussian processes NeurIPS 2023
- T. Pereira, E. Nascimento, L. Resck, D. Mesquita, A. Souza. Distill n' Explain: explaining graph neural networks using simple surrogates AISTATS 2023
- A. Souza, D. Mesquita, S. Kaski, V. Garg. Provably expressive temporal graph networks NeurIPS 2022
- D. de Souza, D. Mesquita, S. Kaski, L. Acerbi. Parallel MCMC without embarrassing failures AISTATS 2022
- K. El Mekkaoui, D. Mesquita, P. Blomstedt, S. Kaski. Federated stochastic gradient Langevin dynamics UAI 2021
- D. Mesquita, A. Souza, S. Kaski. Rethinking pooling in graph neural networks NeurIPS 2020
- D. de Souza, D. Mesquita, J. Gomes, C. Mattos. Learning GPLVM with arbitrary kernels using the unscented transformation AISTATS 2020
- D. Mesquita, P. Blomstedt, S. Kaski. Parallel MCMC using deep invertible transformations UAI 2019