Determinantal point processes (DPPs) arise in random matrix theory and quantum physics as models of random variables with negative correlations. Among many remarkable properties, they offer tractable algorithms for exact inference, including computing marginals, computing certain conditional probabilities, and sampling. DPPs are a natural model for subset selection problems where diversity is preferred. 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. I'll present our recent work on a novel factorization and dual representation of DPPs that enables efficient inference for exponentially-sized structured sets. We develop a new inference algorithm based on Newton identities for DPPs conditioned on subset size. We also derive efficient parameter estimation for DPPs from several types of observations. I'll show
the advantages of the model on several natural language and vision tasks: extractive document summarization, diversifying image search results and multi-person articulated pose estimation problems in images.
Joint work with Alex Kulesza, University of Pennsylvania
Ben Taskar received his bachelor's and doctoral degree in Computer Science from Stanford University. After a postdoc at the University of California at Berkeley, he joined the faculty at the University of Pennsylvania Computer and Information Science Department in 2007, where he currently co-directs PRiML: Penn Research in Machine Learning. His research interests include machine learning, natural language processing and computer vision. He has been awarded the Sloan Research Fellowship and selected for the Young Investigator
Program by the Office of Naval Research and the DARPA Computer Science Study Group. His work on structured prediction has received best paper awards at NIPS and EMNLP conferences.
