| Dual parameterization of sparse variational Gaussian processes V Adam, P Chang, MEE Khan, A Solin Advances in Neural Information Processing Systems 34, 11474-11486, 2021 | 23 | 2021 |
| Fast variational learning in state-space Gaussian process models PE Chang, WJ Wilkinson, ME Khan, A Solin 2020 ieee 30th international workshop on machine learning for signal …, 2020 | 23 | 2020 |
| State space expectation propagation: Efficient inference schemes for temporal Gaussian processes W Wilkinson, P Chang, M Andersen, A Solin International Conference on Machine Learning, 10270-10281, 2020 | 16 | 2020 |
| Fantasizing with dual GPs in Bayesian optimization and active learning PE Chang, P Verma, ST John, V Picheny, H Moss, A Solin arXiv preprint arXiv:2211.01053, 2022 | 4 | 2022 |
| Memory-based dual gaussian processes for sequential learning PE Chang, P Verma, ST John, A Solin, ME Khan International Conference on Machine Learning, 4035-4054, 2023 | 3 | 2023 |
| Sparse Function-space Representation of Neural Networks A Scannell, R Mereu, P Chang, E Tamir, J Pajarinen, A Solin arXiv preprint arXiv:2309.02195, 2023 | 2 | 2023 |
| Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes WJ Wilkinson, PE Chang, MR Andersen, A Solin Second Symposium on Advances in Approximate Bayesian Inference, 2019 | 1 | 2019 |
| Function-space Parameterization of Neural Networks for Sequential Learning A Scannell, R Mereu, P Chang, E Tamir, J Pajarinen, A Solin arXiv preprint arXiv:2403.10929, 2024 | | 2024 |
| Sequential Learning in GPs with Memory and Bayesian Leverage Score P Verma, PE Chang, A Solin, ME Khan Continual Lifelong Learning Workshop at ACML 2022, 2022 | | 2022 |
