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» Learning to rank on graphs
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FOCS
1994
IEEE
15 years 10 months ago
The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs
We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new typ...
Michael A. Bender, Donna K. Slonim
JMLR
2010
144views more  JMLR 2010»
15 years 1 months ago
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables
In this paper we address the problem of predicting when the available data is incomplete. We show that changing the generally accepted table-wise view of the sample items into a g...
Sándor Szedmák, Yizhao Ni, Steve R. ...
COLT
2004
Springer
15 years 12 months ago
Learning a Hidden Graph Using O(log n) Queries Per Edge
We consider the problem of learning a general graph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden grap...
Dana Angluin, Jiang Chen
ICDE
2008
IEEE
146views Database» more  ICDE 2008»
16 years 7 months ago
Explaining and Reformulating Authority Flow Queries
Authority flow is an effective ranking mechanism for answering queries on a broad class of data. Systems have been developed to apply this principle on the Web (PageRank and topic ...
Ramakrishna Varadarajan, Vagelis Hristidis, Louiqa...
NIPS
2004
15 years 7 months ago
A Large Deviation Bound for the Area Under the ROC Curve
The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem. We study large deviation properties of the AUC; in particular, ...
Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan...