Approximate inference for determinantal point processes / Jennifer Gillenwater.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Gillenwater, Jennifer, author.

Contributor:

Taskar, Ben, degree supervisor.

Fox, Emily, degree supervisor.

Kearns, Michael, degree committee member.

Jadbabaie, Ali, degree committee member.

Rakhlin, Alexander, degree committee member.

Bilmes, Jeff, degree committee member.

University of Pennsylvania. Department of Computer and Information Science.

Language:

English

Subjects (All):

Penn dissertations--Computer and information science.

Computer and information science--Penn dissertations.

Local Subjects:

Penn dissertations--Computer and information science.

Computer and information science--Penn dissertations.

Physical Description:

xii, 149 leaves ; 29 cm

Production:

[Philadelphia, Pennsylvania] : [University of Pennsylvania], 2014.

Summary:

In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were originally developed in the physics community to describe the repulsive interactions of fermions. More recently, they have been applied to machine learning problems such as search diversification and document summarization, which can be cast as subset selection tasks. A challenge, however, is scaling such DPP-based methods to the size of the datasets of interest to this community, and developing approximations for DPP inference tasks whose exact computation is prohibitively expensive. A DPP defines a probability distribution over all subsets of a ground set of items. Consider the inference tasks common to probabilistic models, which include normalizing, marginalizing, conditioning, sampling, estimating the mode, and maximizing likelihood. For DPPs, exactly computing the quantities necessary for the first four of these tasks requires time cubic in the number of items or features of the items. In this thesis, we propose a means of making these four tasks tractable even in the realm where the number of items and the number of features is large. Specifically, we analyze the impact of randomly projecting the features down to a lower-dimensional space and show that the variational distance between the resulting DPP and the original is bounded. In addition to expanding the circumstances in which these first four tasks are tractable, we also tackle the other two tasks, the first of which is known to be NP-hard (with no PTAS) and the second of which is conjectured to be NP-hard. For mode estimation, we build on submodular maximization techniques to develop an algorithm with a multiplicative approximation guarantee. For likelihood maximization, we exploit the generative process associated with DPP sampling to derive an expectation-maximization (EM) algorithm. We experimentally verify the practicality of all the techniques that we develop, testing them on applications such as news and research summarization, political candidate comparison, and product recommendation.

Notes:

Ph. D. University of Pennsylvania 2014.

Department: Computer and Information Science.

Supervisor: Ben Taskar, Emily Fox.

Includes bibliographical references.

OCLC:

908655623