
Optimizing QoS for ErasureCoded Wireless Data Centers
Cloud computing facilitates the access of applications and data from any...
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Stochastic Compositional Gradient Descent under Compositional constraints
This work studies constrained stochastic optimization problems where the...
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A PrimalDual Framework for Decentralized Stochastic Optimization
We consider the decentralized convex optimization problem, where multipl...
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Sparse Representations of Positive Functions via Projected PseudoMirror Descent
We consider the problem of expected risk minimization when the populatio...
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Practical Precoding via Asynchronous Stochastic Successive Convex Approximation
We consider stochastic optimization of a smooth nonconvex loss function...
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Conservative Stochastic Optimization with Expectation Constraints
This paper considers stochastic convex optimization problems where the o...
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Dynamic Cache Management In Content Delivery Networks
The importance of content delivery networks (CDN) continues to rise with...
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Distributed Stochastic NonConvex Optimization: MomentumBased Variance Reduction
In this work, we propose a distributed algorithm for stochastic nonconv...
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Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck
Gaussian processes provide a framework for nonlinear nonparametric Bayes...
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A Generalized Framework for Autonomous Calibration of Wheeled Mobile Robots
Robotic calibration allows for the fusion of data from multiple sensors ...
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Parallel Restarted SPIDER – Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
In this paper, we propose a distributed algorithm for stochastic smooth,...
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Model Free Calibration of Wheeled Robots Using Gaussian Process
Robotic calibration allows for the fusion of data from multiple sensors ...
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Optimally Compressed Nonparametric Online Learning
Batch training of machine learning models based on neural networks is no...
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Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony
An open challenge in supervised learning is conceptual drift: a data poi...
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Adaptive Kernel Learning in Heterogeneous Networks
We consider the framework of learning over decentralized networks, where...
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Distributed Inexact Successive Convex Approximation ADMM: AnalysisPart I
In this twopart work, we propose an algorithmic framework for solving n...
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Optimal Design of Queuing Systems via Compositional Stochastic Programming
Welldesigned queuing systems form the backbone of modern communications...
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Online Learning over Dynamic Graphs via Distributed Proximal Gradient Algorithm
We consider the problem of tracking the minimum of a timevarying convex...
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On Socially Optimal Traffic Flow in the Presence of Random Users
Traffic assignment is an integral part of urban city planning. Roads and...
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Decentralized MultiAntenna Coded Caching with Cyclic Exchanges
This paper considers a single cell multiantenna base station delivering...
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Stochastic Multidimensional Scaling
Multidimensional scaling (MDS) is a popular dimensionality reduction tec...
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Dynamic Network Cartography
Communication networks have evolved from specialized, research and tacti...
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Ketan Rajawat
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