Abstract. We advocate to analyze the average complexity of learning problems. An appropriate framework for this purpose is introduced. Based on it we consider the problem of learni...
Stochastic Flow Models (SFMs) are stochastic ystems that abstract the dynamics of complex discrete event systems involving the control of sharable resources. SFMs have been used to...
Supercomputers are prone to frequent faults that adversely affect their performance, reliability and functionality. System logs collected on these systems are a valuable resource o...
Full revelation of private values is impractical in many large-scale markets, where posted price mechanisms are a simpler alternative. In this work, we compare the asymptotic beha...
—We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation t...