632 citations · 1.1k across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured mod…
Multi-View Learning over Structured and Non-Identical Outputs
Kuzman Ganchev, Joao Graca, John Blitzer +1
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, eac…
Learning Determinantal Point Processes
Alex Kulesza, Ben Taskar
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among…