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» Learning Influence Probabilities In Social Networks
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KDD
2010
ACM
188views Data Mining» more  KDD 2010»
13 years 5 months ago
Trust network inference for online rating data using generative models
In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a f...
Freddy Chong Tat Chua, Ee-Peng Lim
ATAL
2008
Springer
13 years 9 months ago
No-regret learning and a mechanism for distributed multiagent planning
We develop a novel mechanism for coordinated, distributed multiagent planning. We consider problems stated as a collection of single-agent planning problems coupled by common soft...
Jan-P. Calliess, Geoffrey J. Gordon
SIGECOM
2009
ACM
114views ECommerce» more  SIGECOM 2009»
14 years 1 months ago
Policy teaching through reward function learning
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent’s decisions by providing limited incentives. In this paper, ...
Haoqi Zhang, David C. Parkes, Yiling Chen
COMCOM
2004
112views more  COMCOM 2004»
13 years 7 months ago
Design and analysis of optimal adaptive de-jitter buffers
In order to transfer voice or some other application requiring real-time delivery over a packet network, we need a de-jitter buffer to eliminate delay jitters. An important design...
Gagan L. Choudhury, Robert G. Cole
KDD
2002
ACM
127views Data Mining» more  KDD 2002»
14 years 7 months ago
Mining knowledge-sharing sites for viral marketing
Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mi...
Matthew Richardson, Pedro Domingos