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We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. We consider approximate solutions obtained by solving ...
A global stochastic search method, which is strictly derivative-free yet directed through a gain-based additive update term, is proposed and applied to the inverse problem of ultrasound modulated opti...
Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and comm...
We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving f...
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterp...
In this paper, we consider unsupervised partitioning problems, such as clustering, image segmentation, video segmentation and other change-point detection problems. We focus on partitioning problems b...
For estimating a lower bounded parametric function in the framework of Marchand and Strawderman(2006), we provide “through” a unified approach a class of Bayesian confidence intervals with credibility...
A common problem in the sciences is that a signal of interest is observed only indirectly, through smooth functionals of the signal whose values are then obscured by noise. In suchinverse problems, th...
A brief presentation of basics of the theory of Tchebycheff and Markov systems of functions and its applications to extremal problems for integrals of such functions is given.
In the Multi-Armed Bandit (MAB) problem, there are a given set of arms with unknown reward distributions. At each time, a player selects one arm to play, aiming to maximize the total expected reward o...
We consider a Kullback-Leibler-based algorithmfor the stochastic multi-armed bandit prob- lem in the case of distributions with finite supports (not necessarily known beforehand), whose asymptotic r...
We consider the inverse problem of estimating a function u from noisy, possibly nonlinear, observations. We adopt a Bayesian approach to the problem and widen the existing theory, which is developed...
A trend in compressed sensing (CS) is to exploit struc- ture for improved reconstruction performance. In the basic CS model (i.e. the single measurement vec- tor model), exploiting the clustering s...
A number of regularization methods for discrete inverse problems consist in considering weighted versions of the usual least square solution. However, these so-called filter methods are generally res...
In applications throughout science and engineering one is often faced with the challenge of solving an ill-posed inverse problem, where the number of available measurements is smaller than the dimensi...

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