@inproceedings{discovery10180942,
         journal = {Proceedings of Machine Learning Research},
           title = {Bayesian online change point detection with Hilbert space approximate Student-t process},
            year = {2023},
           pages = {30553--30569},
            note = {{\copyright} The Authors 2023. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).},
       booktitle = {Proceedings of the 40th International Conference on Machine Learning},
       publisher = {PMLR (Proceedings of Machine Learning Research)},
          volume = {202},
            issn = {2640-3498},
        abstract = {In this paper, we introduce a variant of Bayesian online change point detection with a reduced-rank Student-t process (TP) and dependent Student-t noise, as a nonparametric time series model. Our method builds and improves upon the state-of-the-art Gaussian process (GP) change point model benchmark of Saat{\cc}i et al. (2010). The Student-t process generalizes the concept of a GP and hence yields a more flexible alternative. Additionally, unlike a GP, the predictive variance explicitly depends on the training observations, while the use of an entangled Student-t noise model preserves analytical tractability. Our approach also uses a Hilbert space reduced-rank representation of the TP kernel, derived from an eigenfunction expansion of the Laplace operator (Solin \& S{\"a}rkk{\"a}, 2020), to alleviate its computational complexity. Improvements in prediction and training time are demonstrated with real-world data sets.},
             url = {https://proceedings.mlr.press/v202/sellier23a.html},
          author = {Sellier, J and Dellaportas, P}
}