
Distributed Online Linear Quadratic Control for Linear Timeinvariant Systems
Classical linear quadratic (LQ) control centers around linear timeinvar...
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Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuoustime Dynamics
This work addresses distributed optimization, where a network of agents ...
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Unconstrained Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems
The regret bound of dynamic online learning algorithms is often expresse...
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Learning from NonIID Data in Hilbert Spaces: An Optimal Recovery Perspective
The notion of generalization in classical Statistical Learning is often ...
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Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs
We propose a novel GAN framework for nonparametric density estimation w...
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Distributed Projected Subgradient Method for Weakly Convex Optimization
The stochastic subgradient method is a widelyused algorithm for solving...
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Statistical and Topological Properties of Sliced Probability Divergences
The idea of slicing divergences has been proven to be successful when co...
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Generalized Sliced Distances for Probability Distributions
Probability metrics have become an indispensable part of modern statisti...
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A RandomFeature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Space
In supervised learning using kernel methods, we encounter a largescale ...
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Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Despite their success, kernel methods suffer from a massive computationa...
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Cell Association via Boundary Detection: A Scalable Approach Based on DataDriven Random Features
The problem of cell association is considered for cellular users present...
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A General Scoring Rule for Randomized Kernel Approximation with Application to Canonical Correlation Analysis
Random features has been widely used for kernel approximation in larges...
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A MeanField Theory for Kernel Alignment with Random Features in Generative Adversarial Networks
We propose a novel supervised learning method to optimize the kernel in ...
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Distributed Parameter Estimation in Randomized Onehiddenlayer Neural Networks
This paper addresses distributed parameter estimation in randomized one...
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On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees
Random features provide a practical framework for largescale kernel app...
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Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Nonlinear kernels can be approximated using finitedimensional feature m...
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On DataDependent Random Features for Improved Generalization in Supervised Learning
The randomizedfeature approach has been successfully employed in large...
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On Optimal Generalizability in Parametric Learning
We consider the parametric learning problem, where the objective of the ...
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Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits
We address the Mbestarm identification problem in multiarmed bandits....
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An Online Optimization Approach for MultiAgent Tracking of Dynamic Parameters in the Presence of Adversarial Noise
This paper addresses tracking of a moving target in a multiagent networ...
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Distributed Online Optimization in Dynamic Environments Using Mirror Descent
This work addresses decentralized online optimization in nonstationary ...
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On Sequential Elimination Algorithms for BestArm Identification in MultiArmed Bandits
We consider the bestarm identification problem in multiarmed bandits, ...
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Learning without Recall by Random Walks on Directed Graphs
We consider a network of agents that aim to learn some unknown state of ...
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Switching to Learn
A network of agents attempt to learn some unknown state of the world dra...
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Online Optimization : Competing with Dynamic Comparators
Recent literature on online learning has focused on developing adaptive ...
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Distributed Detection : Finitetime Analysis and Impact of Network Topology
This paper addresses the problem of distributed detection in multiagent...
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Online Learning of Dynamic Parameters in Social Networks
This paper addresses the problem of online learning in a dynamic setting...
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Exponentially Fast Parameter Estimation in Networks Using Distributed Dual Averaging
In this paper we present an optimizationbased view of distributed param...
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Shahin Shahrampour
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Postdoctoral researcher interested in problems at the interface of Machine Learning, Optimization and Distributed Algorithms