Maximum Likelihood for Gaussian Process Classifiers under Case-Control Sampling
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Updated
Jul 6, 2022 - Python
Maximum Likelihood for Gaussian Process Classifiers under Case-Control Sampling
[done] phd thesis @ oxford stats
Personal Website with Blogposts, Achievements and Ideas
Probabilistic approach to neural nets - modern scalable approximate inference methods
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
A package to perform EP inference in a variety of settings
Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.
Advanced Message Passing
Expectation Particle Belief Propagation code
UAI 2015. Kernel-based just-in-time learning for expectation propagation
Tree Approximate Message Passing
GPstuff - Gaussian process models for Bayesian analysis
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