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A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning
Specifically, we consider a distributionally robust optimization of the kernel-target alignment with respect to the distribution of training samples over a distributional ball defined by the Kullback-Leibler (KL) divergence.
On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes
We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a given mixture policy class characterized by the convex hull of a set of known base policies.
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve the derived finite dimensional optimization problem.
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images.
A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions
In particular, we prove that in the scaling limits, the empirical measure of the Langevin particles converges to the law of a reflected It\^{o} diffusion-drift process.
A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adverserial Networks
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve finite dimensional optimization problems.
