Contrast Estimation for Parametric Stationary Determinantal Point Processes
نویسندگان
چکیده
منابع مشابه
Rates of estimation for determinantal point processes
Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. In this paper, we study the local geometry of the expected log-likelihood f...
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Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. The difficulty is that it is a non-concave maximization problem, and such f...
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When one is faced with a dataset too large to be used all at once, an obvious solution is to retain only part of it. In practice this takes a wide variety of different forms, but among them “coresets” are especially appealing. A coreset is a (small) weighted sample of the original data that comes with a guarantee: that a cost function can be evaluated on the smaller set instead of the larger on...
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A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y . For example, we might use a DPP to display a set of news headlines that are relevant to a user’s interests while covering a variety of topics. Suppose, however, th...
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ژورنال
عنوان ژورنال: Scandinavian Journal of Statistics
سال: 2016
ISSN: 0303-6898,1467-9469
DOI: 10.1111/sjos.12249