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SIGIR
2011
ACM
12 years 10 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
PODS
2012
ACM
244views Database» more  PODS 2012»
11 years 9 months ago
A rigorous and customizable framework for privacy
In this paper we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized t...
Daniel Kifer, Ashwin Machanavajjhala
KDD
2005
ACM
182views Data Mining» more  KDD 2005»
14 years 7 months ago
Making holistic schema matching robust: an ensemble approach
The Web has been rapidly "deepened" by myriad searchable databases online, where data are hidden behind query interfaces. As an essential task toward integrating these m...
Bin He, Kevin Chen-Chuan Chang
WSDM
2009
ACM
191views Data Mining» more  WSDM 2009»
14 years 2 months ago
Generating labels from clicks
The ranking function used by search engines to order results is learned from labeled training data. Each training point is a (query, URL) pair that is labeled by a human judge who...
Rakesh Agrawal, Alan Halverson, Krishnaram Kenthap...
RTAS
1998
IEEE
13 years 11 months ago
End-Host Architecture for QoS-Adaptive Communication
Proliferation of communication-intensive real-time applications with elastic" timeliness constraints, such as streaming stored video, requires a new design for endhost commun...
Tarek F. Abdelzaher, Kang G. Shin