University of Cambridge > Talks.cam > Machine Learning @ CUED > Bayesian nonparametrics: Dependency and Constraint Modeling

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Bayesian nonparametrics: Dependency and Constraint Modeling

Download to your calendar using vCal

If you have a question about this talk, please contact Zoubin Ghahramani .

Note: TALK via Skype!

In this talk I will briefly overview my research during my PhD, and focus on two works I have done. One is on Bayesian nonparametric dependency modelling with dependent normalized random measures. The construction of these dependency models allows easy manipulating of the dependency structures as well as efficient posterior inference. As a second work, I will talk about how to include additional constraints into Bayesian nonparametric models via the Regularized Bayesian Inference (RegBayes). Specifically, I will introduce the Bayesian max-margin clustering framework derived from RegBayes, which has shown significant improvements over other baselines. Finally future plan will be discussed.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

VGB Room GS15 at the William Gates Building and on Zoom: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon Buckingham House, Murray Edwards College (formerly New Hall) and Anglia Ruskin University

Other talks

Machine Learning for Spectral Geometry and Vice Versa TBC Cambridge AI in Medicine Seminar - July 2026 Group Work Decoding genomic chaos - a pancancer compendium of chromosomal instability Studying viruses to understand what makes pandemic viruses special and how innate immunity works
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity