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» Estimating functional coverage in bounded model checking
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CCE
2004
13 years 6 months ago
Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique
This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-st...
José María Ponce-Ortega, Vicente Ric...
NIPS
1998
13 years 7 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
CDC
2008
IEEE
142views Control Systems» more  CDC 2008»
14 years 27 days ago
Convergence of rule-of-thumb learning rules in social networks
— We study the problem of dynamic learning by a social network of agents. Each agent receives a signal about an underlying state and communicates with a subset of agents (his nei...
Daron Acemoglu, Angelia Nedic, Asuman E. Ozdaglar
APNOMS
2006
Springer
13 years 10 months ago
An Enhanced RED-Based Scheme for Differentiated Loss Guarantees
Abstract. Recently, researchers have explored to provide a queue management scheme with differentiated loss guarantees for the future Internet. The Bounded Random Drop (BRD), known...
Jahwan Koo, Vladimir V. Shakhov, Hyunseung Choo
ATAL
2010
Springer
13 years 7 months ago
On the role of distances in defining voting rules
A voting rule is an algorithm for determining the winner in an election, and there are several approaches that have been used to justify the proposed rules. One justification is t...
Edith Elkind, Piotr Faliszewski, Arkadii M. Slinko