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» Sampling Bounds for Stochastic Optimization
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UAI
2004
13 years 11 months ago
PAC-learning Bounded Tree-width Graphical Models
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Prev...
Mukund Narasimhan, Jeff A. Bilmes
UAI
2004
13 years 11 months ago
Bidding under Uncertainty: Theory and Experiments
This paper describes a study of agent bidding strategies, assuming combinatorial valuations for complementary and substitutable goods, in three auction environments: sequential au...
Amy R. Greenwald, Justin A. Boyan
TON
2010
147views more  TON 2010»
13 years 8 months ago
Coverage-time optimization for clustered wireless sensor networks: a power-balancing approach
—In this paper, we investigate the maximization of the coverage time for a clustered wireless sensor network (WSN) by optimal balancing of power consumption among cluster heads (...
Tao Shu, Marwan Krunz
NIPS
1998
13 years 11 months ago
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
Michael J. Kearns, Satinder P. Singh
SIGECOM
2011
ACM
232views ECommerce» more  SIGECOM 2011»
13 years 21 days ago
Near optimal online algorithms and fast approximation algorithms for resource allocation problems
We present algorithms for a class of resource allocation problems both in the online setting with stochastic input and in the offline setting. This class of problems contains man...
Nikhil R. Devanur, Kamal Jain, Balasubramanian Siv...