| Gaussian Process Latent Variable Alignment Learning I Kazlauskaite, CH Ek, NDF Campbell The 22nd International Conference on Artificial Intelligence and Statistics, 2019 | 8 | 2019 |
| Compositional uncertainty in deep Gaussian processes I Ustyuzhaninov, I Kazlauskaite, M Kaiser, E Bodin, NDF Campbell, CH Ek arXiv preprint arXiv:1909.07698, 2019 | 2 | 2019 |
| Monotonic Gaussian Process Flow I Ustyuzhaninov, I Kazlauskaite, CH Ek, NDF Campbell International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 2 | 2019 |
| Bayesian nonparametric shared multi-sequence time series segmentation O Mikheeva, I Kazlauskaite, H Kjellström, CH Ek arXiv preprint arXiv:2001.09886, 2020 | | 2020 |
| Modulating Surrogates for Bayesian Optimization E Bodin, M Kaiser, I Kazlauskaite, Z Dai, NDF Campbell, CH Ek arXiv, arXiv: 1906.11152, 2019 | | 2019 |
| Modulated Bayesian Optimization using Latent Gaussian Process Models E Bodin, M Kaiser, I Kazlauskaite, NDF Campbell, CH Ek https://arxiv.org/abs/1906.11152, 2019 | | 2019 |
| Sequence Alignment with Dirichlet Process Mixtures I Kazlauskaite, I Ustyuzhaninov, CH Ek, NDF Campbell NeurIPS, Workshop on Bayesian Nonparametrics (BNP@NeurIPS), 2018, 2018 | | 2018 |
| Learning Alignments from Latent Space Structures I Kazlauskaite, CH Ek, N Campbell NIPS Workshop on Learning in High Dimensions with Structure, 2016 | | 2016 |
| Monotonic Gaussian Process Flows: Supplementary material I Ustyuzhaninov, I Kazlauskaite, CH Ek, NDF Campbell | | |
Carl Henrik EkUniversity of CambridgeVerified email at cam.ac.uk
