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ML Lunch (Feb 10): Large Scale Inference of Determinantal Point Processes (DPPs)

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Published on Feb 10, 2014

Speaker: Raja Hafiz Affandi
University of Pennsylvania

Abstract
Determinantal Point Processes (DPPs) are random point processes well-suited for modelling repulsion. In machine learning and statistics, DPPs are a natural model for subset selection problems where diversity is desired. For example, they can be used to select diverse sets of sentences to form document summaries, or to return relevant but varied text and image search results, or to detect non-overlapping multiple object trajectories in video. Among many remarkable properties, they offer tractable algorithms for exact inference, including computing marginals, computing conditional probabilities, and sampling. In our recent work, we extended these algorithms to approximately infer non-linear DPPs defined over a large amount of data, as well as DPPs defined on continuous spaces using low-rank approximations. We demonstrated the advantages of our models on several machine learning and statistical tasks: motion capture video summarization, repulsive mixture modelling and synthesizing diverse human poses. Given time, I will also briefly touch on our other related works such as extending DPPs into a temporal process that sequentially select multiple diverse subsets across time and how we go about learning the parameters of a DPP kernel. These are joint works with Emily Fox, Ben Taskar and Alex Kulesza.

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