Determinantal Point Processes for Machine Learning
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منابع مشابه
Advances in the Theory of Determinantal Point Processes
The theory of determinantal point processes has its roots in work in mathematical physics in the 1960s, but it is only in recent years that it has been developed beyond several specific examples. While there is a rich probabilistic theory, there are still many open questions in this area, and its applications to statistics and machine learning are still largely unexplored. Our contributions are...
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Determinantal point processes (DPPs) are an important concept in random matrix theory and combinatorics. They have also recently attracted interest in the study of numerical methods for machine learning, as they offer an elegant “missing link” between independent Monte Carlo sampling and deterministic evaluation on regular grids, applicable to a general set of spaces. This is helpful whenever a...
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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. In this paper, we study the local geometry of the expected log-likelihood f...
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Determinantal Point Processes (DPPs) have attracted significant interest from the machine-learning community due to their ability to elegantly and tractably model the delicate balance between quality and diversity of sets. We consider learning DPPs from data, a key task for DPPs; for this task, we introduce a novel optimization problem, Contrastive Estimation (CE), which encodes information abo...
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Probabilistic topics models such as Latent Dirichlet Allocation (LDA) provide a useful and elegant tool for discovering hidden structure within large data sets of discrete data, such as corpuses of text. However, LDA implicitly discovers topics along only a single dimension. Recent research on multi-dimensional topic modeling aims to devise techniques that can discover multiple groups of topics...
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عنوان ژورنال:
- Foundations and Trends in Machine Learning
دوره 5 شماره
صفحات -
تاریخ انتشار 2012