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University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Combinatorial Stochastic Processes in Bayesian Nonparametrics
Combinatorial Stochastic Processes in Bayesian NonparametricsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Rowan McAllister. Abstract: The tutorial will cover the following topics: Random partitions. Random tree structures. Random fragmentation and coagulation processes. These models have found usage in clustering and density modeling applications. I will show you that each one of these structures may be constructed from the previous. Along the way, we will also cover how to study these objects from the perspective of random measures, and we’ll see why that perspective is so helpful, not least because it helps us to better understand exchangeability. The next topic is random feature allocations. We will develop this theory, which will seem to merely mimic the theory of random partitions. However, I will in fact show you how to construct random feature allocations from random partitions. This opens the door to constructing random feature allocations from random tree structures, random fragmentations/coagulations, and so on. The potential applications for these models will be clear. The theme of the talk is using simple models to construct more elaborate ones. The final bits of the talk are based on current work with Zoubin Ghahramani and Daniel M. Roy. For preliminary reading:
This talk is part of the Machine Learning Reading Group @ CUED series. This talk is included in these lists:
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