632 citations · 1.1k across the 9 of their papers we have counts for
9 papers
Expectation-Maximization for Learning Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Emily Fox +1
A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we wo…
Learning the Parameters of Determinantal Point Process Kernels
Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams +1
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing p…
Controlling Complexity in Part-of-Speech Induction
João V. Graça, Kuzman Ganchev, Luisa Coheur +2
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this…
Approximate Inference in Continuous Determinantal Point Processes
Raja Hafiz Affandi, Emily B. Fox, Ben Taskar
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset se…
Discriminative Probabilistic Models for Relational Data
Ben Taskar, Pieter Abbeel, Daphne Koller
In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classificati…
Structured Prediction Cascades
David Weiss, Benjamin Sapp, Ben Taskar
Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference in…