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ICML
2004
IEEE
14 years 10 months ago
Learning associative Markov networks
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bio...
Benjamin Taskar, Vassil Chatalbashev, Daphne Kolle...
ESANN
1998
13 years 11 months ago
Lazy learning for control design
This paper presents two local methods for the control of discrete-time unknown nonlinear dynamical systems, when only a limited amount of input-output data is available. The modeli...
Gianluca Bontempi, Mauro Birattari, Hugues Bersini
FOCS
2005
IEEE
14 years 3 months ago
Mechanism Design via Machine Learning
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a wide variety of r...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
UAI
2008
13 years 11 months ago
Learning and Solving Many-Player Games through a Cluster-Based Representation
In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. ...
Sevan G. Ficici, David C. Parkes, Avi Pfeffer
AAAI
2006
13 years 11 months ago
Supporting Queries with Imprecise Constraints
In this paper, we motivate the need for and challenges involved in supporting imprecise queries over Web databases. Then we briefly explain our solution, AIMQ - a domain independe...
Ullas Nambiar, Subbarao Kambhampati