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» Learning and Generalization with the Information Bottleneck
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AAAI
2006
14 years 11 months ago
Preference Elicitation and Generalized Additive Utility
Any automated decision support software must tailor its actions or recommendations to the preferences of different users. Thus it requires some representation of user preferences ...
Darius Braziunas, Craig Boutilier
SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
13 years 2 days ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
70
Voted
UIST
2005
ACM
15 years 3 months ago
Preference elicitation for interface optimization
Decision-theoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck — in most c...
Krzysztof Gajos, Daniel S. Weld
NAACL
2010
14 years 7 months ago
Constraint-Driven Rank-Based Learning for Information Extraction
Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rankbased lear...
Sameer Singh, Limin Yao, Sebastian Riedel, Andrew ...