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Gradient-based simulation optimization under probability constraints
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Asynchronous distributed principal component analysis using stochastic approx...
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Aircraft classification with a low resolution infrared sensor
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Central Limit Theorems for Stochastic Approximation with controlled Markov ch...
This paper provides a Central Limit Theorem (CLT) for a process $\{\theta_n, n\geq 0\}$ satisfying a stochastic approximation (SA) equation of the form $\theta_{n+1} =... -
Distributed algorithms in autonomous and heterogeneous networks
Growing diversity of agents in current communication networks and increasing capacitiesof concurrent technologies in the network environment has lead to the... -
Particle approximation and the Laplace method for Bayesian filtering
The thesis deals with the contribution of the Laplace method to the approximation of the Bayesian filter in hidden Markov models with continuous state--space, i.e. in... -
Interference coordination in wireless networks: A flow-level perspective
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Development of methods to analyze data streams
High dimensional data are supposed to be independent on-line observations of a random vector. In the second chapter, the latter is denoted by Z and sliced into two... -
Almost sure convergence of stochastic gradient processes with matrix step sizes
We consider a stochastic gradient process, which is a special case of stochastic approximation process, where the positive real step size a_{n} is replaced by a random... -
Adaptivity of averaged stochastic gradient descent to local strong convexity ...
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Robust Stochastic Approximation Approach to Stochastic Programming
International audience
