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» Online Bounds for Bayesian Algorithms
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COLT
2010
Springer
14 years 7 months ago
Regret Minimization With Concept Drift
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...
Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jenni...
AI
2002
Springer
14 years 9 months ago
The size distribution for Markov equivalence classes of acyclic digraph models
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
Steven B. Gillispie, Michael D. Perlman
ALGORITHMICA
2004
111views more  ALGORITHMICA 2004»
14 years 9 months ago
Non-Clairvoyant Scheduling for Minimizing Mean Slowdown
We consider the problem of scheduling dynamically arriving jobs in a non-clairvoyant setting, that is, when the size of a job in remains unknown until the job finishes execution. ...
Nikhil Bansal, Kedar Dhamdhere, Jochen Könema...
WWW
2007
ACM
15 years 10 months ago
Optimized query planning of continuous aggregation queries in dynamic data dissemination networks
Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some ...
Rajeev Gupta, Krithi Ramamritham
IPPS
2006
IEEE
15 years 3 months ago
QoS-based management of multiple shared resource in dynamic real-time systems
Dynamic real-time systems require adaptive resource management to accommodate varying processing needs. We address the problem of resource management with multiple shared resource...
Klaus H. Ecker, Frank Drews, Jens Lichtenberg