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The performance of fast forward-backward splitting, or equivalently fast proximal gradient methods, is susceptible to conditioning of the optimization problem data. This conditioning is related to a m...
Fast gradient methods are known to be non-monotone algorithms, and oscillations typically occur around the solution. To avoid this behavior, we propose in this paper a fast gradient method with restar...
First order optimization methods often perform poorly on ill-conditioned optimization problems. However, by preconditioning the problem data and solving the preconditioned problem, the performance of ...
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
What is the response of the climate system to anthropogenic forcings? This question is addressed typically using projections from climate models. The uncertainty surrounding current climate projection...
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited ...
Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simu-lating from the posterior d...
In this paper, we study the nonnegative matrix factorization problem under the separability assumption (that is, there exists a cone spanned by a small subsetof the columns of the input nonnegative da...
To recover a sparse signal from an underdetermined system, we often solve a constrained`1-norm minimization problem. In many cases, the signal sparsity and the recovery performance can be further impr...
We describe a new optimization scheme for nding high-quality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the op...
A new procedure, calledDDα-procedure, is developed to solve the problem of classifyingd-dimensional objects into q≥2 classes. The pro-cedure is completely nonparametric; it usesq-dimensional depth plo...
We propose a new algorithm to compute numerically the distribution function of the sum of $d$ dependent, non-negative random variables with given joint distribution.
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm.

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