Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation...

We present an overview of recent nonperturbative results in the theory of heat kernel and its late time asymptotics responsible for the infrared behavior of quantum effective action for massless theories. In particular, we derive the generalization of the Coleman-Weinberg potential to physical situations when the field is not homogeneous throughout the whole spacetime. This generalization repre...

I discuss radiative corrections to the BFKL equation for high energy cross sections in perturbative QCD. Due to the gluon Reggeization in the next-to-leading ln s approximation, the form of the BFKL equation remains unchanged and the corrections to the BFKL kernel are expressed in terms of the two-loop contribution to the gluon Regge trajectory, the one-loop correction to the Reggeon-Reggeon-gl...

This paper studies the issue of optimal deconvolution density estimation using wavelets. We explore the asymptotic properties of estimators based on thresholding of estimated wavelet coe cients. Minimax rates of convergence under the integrated square loss are studied over Besov classes B pq of functions for both ordinary smooth and supersmooth convolution kernels. The minimax rates of converge...

the early cytokines production in response to live logarithmic and stationary phase promastigotes of leishmania major was examined on the peripheral blood cells of the healthy individuals. whole blood cultures were stimulated by either logarithmic or stationary phase promastigotes. ifn- and il-10 productions were assessed by specific sandwich elisa. the results showed that the logarithmic prom...

On the manifold of positive definite matrices, a Riemannian metric Kφ is associated with a positive kernel function φ on (0,∞) × (0,∞) by defining K D(H,K) = ∑ i,j φ(λi, λj) TrPiHPjK, where D is a foot point with the spectral decomposition D = ∑ i λiPi and H,K are Hermitian matrices (tangent vectors). We are concerned with the case φ(x, y) = M(x, y)θ where M(x, y) is a mean of scalars x, y > 0....

We present an overview of recent nonperturbative results in the theory of heat kernel and its late time asymptotics responsible for the infrared behavior of quantum effective action for massless theories. In particular, we derive the generalization of the Coleman-Weinberg potential to physical situations when the field is not homogeneous throughout the whole spacetime. This generalization repre...

We investigate data based procedures for selecting the kernel when learning with Support Vector Machines. We provide generalization error bounds by estimating the Rademacher complexities of the corresponding function classes. In particular we obtain a complexity bound for function classes induced by kernels with given eigenvectors, i.e., we allow to vary the spectrum and keep the eigenvectors f...

The paper examines the problem of computing the potential and the attraction force of an ellipsoid, which requires taking a triple integral with an analytically integrable kernel. We consider the kernel as a weight function, while the inner integral is approximated by a quadrature for the product of functions, one of which has an integrable singularity. Such an approach makes it possible to obt...

In this paper we present a generic primal-dual interior point methods (IPMs) for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. The proposed kernel function does not satisfy all the conditions proposed in [2]. We show that the corresponding large-update algorithm improves the ite...