| Variational inference for graph convolutional networks in the absence of graph data and adversarial settings P Elinas, EV Bonilla, L Tiao Advances in neural information processing systems 33, 18648-18660, 2020 | 76 | 2020 |
| Model-based asynchronous hyperparameter and neural architecture search A Klein, LC Tiao, T Lienart, C Archambeau, M Seeger arXiv preprint arXiv:2003.10865, 2020 | 48 | 2020 |
| BORE: Bayesian optimization by density-ratio estimation LC Tiao, A Klein, MW Seeger, EV Bonilla, C Archambeau, F Ramos International Conference on Machine Learning, 10289-10300, 2021 | 32* | 2021 |
| Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference LC Tiao, EV Bonilla, F Ramos arXiv preprint arXiv:1806.01771, 2018 | 32 | 2018 |
| Variational graph convolutional networks L Tiao, P Elinas, H Nguyen, EV Bonilla Proc. Graph Rep. Learn. Workshop, 2019 | 10* | 2019 |
| Batch Bayesian optimisation via density-ratio estimation with guarantees R Oliveira, L Tiao, FT Ramos Advances in Neural Information Processing Systems 35, 29816-29829, 2022 | 4 | 2022 |
| Spherical inducing features for orthogonally-decoupled gaussian processes LC Tiao, V Dutordoir, V Picheny International Conference on Machine Learning, 34143-34160, 2023 | 1 | 2023 |
| Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings: Supplementary Material P Elinas, EV Bonilla, L Tiao | | |
| Supplementary Material BORE: Bayesian Optimization by Density-Ratio Estimation LC Tiao, A Klein, M Seeger, EV Bonilla, C Archambeau, F Ramos | | |
Edwin V. BonillaPrincipal Research Scientist, CSIRO's Data61在 data61.csiro.au 的电子邮件经过验证
