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WWW
2011
ACM
13 years 21 days ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
WWW
2010
ACM
14 years 23 days ago
Sampling high-quality clicks from noisy click data
Click data captures many users’ document preferences for a query and has been shown to help significantly improve search engine ranking. However, most click data is noisy and of...
Adish Singla, Ryen W. White
JMLR
2010
128views more  JMLR 2010»
13 years 17 days ago
Fluid Dynamics Models for Low Rank Discriminant Analysis
We consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minim...
Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee
AAAI
2008
13 years 8 months ago
Hypothesis Pruning and Ranking for Large Plan Recognition Problems
This paper addresses the problem of plan recognition for multi-agent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. ...
Gita Sukthankar, Katia P. Sycara
CORR
2010
Springer
115views Education» more  CORR 2010»
13 years 4 months ago
Tight oracle bounds for low-rank matrix recovery from a minimal number of random measurements
This paper presents several novel theoretical results regarding the recovery of a low-rank matrix from just a few measurements consisting of linear combinations of the matrix entr...
Emmanuel J. Candès, Yaniv Plan