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» Learning Taxonomies by Dependence Maximization
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TNN
2010
155views Management» more  TNN 2010»
15 years 1 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
COLT
2010
Springer
15 years 4 months ago
Nonparametric Bandits with Covariates
We consider a bandit problem which involves sequential sampling from two populations (arms). Each arm produces a noisy reward realization which depends on an observable random cov...
Philippe Rigollet, Assaf Zeevi
EMNLP
2008
15 years 8 months ago
Selecting Sentences for Answering Complex Questions
Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topicoriented, informative multi-document summarization. I...
Yllias Chali, Shafiq R. Joty
ICML
2010
IEEE
15 years 7 months ago
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is ...
Niranjan Srinivas, Andreas Krause, Sham Kakade, Ma...
204
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EDM
2009
147views Data Mining» more  EDM 2009»
15 years 4 months ago
Using Dirichlet priors to improve model parameter plausibility
Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better u...
Dovan Rai, Yue Gong, Joseph Beck