Search Results for author: Masoud Badiei Khuzani

Found 6 papers, 0 papers with code

A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions

no code implementations23 Jun 2020 Masoud Badiei Khuzani, Yinyu Ye, Sandy Napel, Lei Xing

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.

Image Retrieval Retrieval +1

A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adverserial Networks

no code implementations25 Sep 2019 Masoud Badiei Khuzani, Liyue Shen, Shahin Shahrampour, Lei Xing

We subsequently leverage a particle stochastic gradient descent (SGD) method to solve finite dimensional optimization problems.

A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models

no code implementations25 Sep 2019 Masoud Badiei Khuzani, Liyue Shen, Shahin Shahrampour, Lei Xing

We subsequently leverage a particle stochastic gradient descent (SGD) method to solve the derived finite dimensional optimization problem.

Two-sample testing

On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes

no code implementations15 Mar 2019 Masoud Badiei Khuzani, Varun Vasudevan, Hongyi Ren, Lei Xing

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 Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning

no code implementations27 Feb 2019 Masoud Badiei Khuzani, Hongyi Ren, Md Tauhidul Islam, Lei Xing

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.

Generalization Bounds Model Selection +2

Cannot find the paper you are looking for? You can Submit a new open access paper.