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Title: | NEW ADVANCES IN BAYESIAN INFERENCE FOR GAUSSIAN PROCESS AND DEEP GAUSSIAN PROCESS MODELS | Authors: | YU HAIBIN | ORCID iD: | orcid.org/0000-0001-7723-6511 | Keywords: | Bayeisan machine learning, Gaussian process, deep Gaussian process, variational inference | Issue Date: | 17-Jan-2020 | Citation: | YU HAIBIN (2020-01-17). NEW ADVANCES IN BAYESIAN INFERENCE FOR GAUSSIAN PROCESS AND DEEP GAUSSIAN PROCESS MODELS. ScholarBank@NUS Repository. | Abstract: | Machine learning is the study of methods for computers to perform a specific task in a data-driven manner. In particular, Bayesian machine learning has attracted enormous attention mainly due to the ability to provide uncertainty estimates following Bayesian inference. This thesis focuses on Gaussian processes (GPs), a rich class of Bayesian nonparametric models for performing Bayesian machine learning with formal measures of predictive uncertainty. However, the applicability of GP in large datasets and in hierarchical composition of GPs is severely limited by computational issues and intractabilities. Therefore, it is crucial to develop accurate and efficient inference algorithms to address these challenges. To this end, this thesis aims at proposing a series of novel approximate Bayesian inference methods for a wide variety of GP models, which unifies the previous literature, significantly extends them, and hopefully lays the foundation for future inference methods. | URI: | https://scholarbank.nus.edu.sg/handle/10635/168231 |
Appears in Collections: | Ph.D Theses (Open) |
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