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NIPS
2003
14 years 10 days ago
Self-calibrating Probability Forecasting
In the problem of probability forecasting the learner’s goal is to output, given a training set and a new object, a suitable probability measure on the possible values of the ne...
Vladimir Vovk, Glenn Shafer, Ilia Nouretdinov
UAI
2003
14 years 9 days ago
Learning Module Networks
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
ICML
2010
IEEE
14 years 1 days ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh
CORR
2010
Springer
147views Education» more  CORR 2010»
13 years 11 months ago
Modeling the structure and evolution of discussion cascades
We analyze the structure and evolution of discussion cascades in four popular websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big heterogeneities between these ...
Vicenç Gómez, Hilbert J. Kappen, And...
DC
2006
13 years 11 months ago
Practical load balancing for content requests in peer-to-peer networks
This paper studies the problem of balancing the demand for content in a peer-to-peer network across heterogeneous peer nodes that hold replicas of the content. Previous decentraliz...
Mema Roussopoulos, Mary Baker