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Coordinate-Descent Diffusion Learning by Networked Agents

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Multi-index Monte Carlo: when sparsity meets sampling

Fabio Nobile

We propose and analyze a novel Multi Index Monte Carlo (MIMC) method for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The MIMC method is both a s ...

2016

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper examines the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic g ...

IEEE2016

Distributed Policy Evaluation Under Multiple Behavior Strategies

Ali H. Sayed

We apply diffusion strategies to develop a fully-distributed cooperative reinforcement learning algorithm in which agents in a network communicate only with their immediate neighbors to improve predictions about their environment. The algorithm can also be ...

IEEE2015

Toward direct light-to-digital conversion using a pulse-driven hybrid MOS-PN photodetector

Maher Kayal, Adil Koukab, Denis Sallin

In this Letter, a direct light-to-digital converter based on an MOS-PN photodetector driven by pulsed voltage is presented. The objective is to avoid any analog-to-digital or time-to-digital conversion and, thereby, to pave the way for a new generation of ...

Optical Society of America2015

XOR-Based Encoding With Instantaneous Decoding for the Broadcast Erasure Channel With Feedback: The Three-User Case

We study the case of a three-user broadcast erasure channel with multiple unicast traffic sessions, where feedback from the users is fed back to the transmitter in the form of positive acknowledgment (ACK)/negative acknowledgment (NACK) messages. The capac ...

Institute of Electrical and Electronics Engineers2014

Matrix Recipes for Hard Thresholding Methods

Volkan Cevher, Anastasios Kyrillidis

In this paper, we present and analyze a new set of low-rank recovery algorithms for linear inverse problems within the class of hard thresholding methods. We provide strategies on how to set up these algorithms via basic ingredients for different configura ...

Springer Verlag2014

Multi-Objective Quality-Driven Service Selection-A Fully Polynomial Time Approximation Scheme

Boi Faltings, Immanuel Trummer

The goal of multi-objective quality-driven service selection (QDSS) is to find service selections for a workflow whose quality-of-service (QoS) values are Pareto-optimal. We consider multiple QoS attributes such as response time, cost, and reliability. A s ...

Institute of Electrical and Electronics Engineers2014

Functionally Optimized Neuritogenic Farinosone C Analogs: SAR-Study and Investigations on Their Mode of Action

Karl Gademann, Patrick Burch, Andrea Chicca

Several natural products derived from entomopathogenic fungi have been shown to initiate neuronal differentiation in the rat pheochromocytoma PC12 cell line. After the successful completion of the total synthesis program, the reduction of structural comple ...

Amer Chemical Soc2014

Adaptive Networks

Ali H. Sayed

This paper surveys recent advances related to adaptation, learning, and optimization over networks. Various distributed strategies are discussed that enable a collection of networked agents to interact locally in response to streaming data and to continual ...

IEEE2014

A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs

Daniel Kuhn

We consider quadratic stochastic programs with random recourse—a class of problems which is perceived to be computationally demanding. Instead of using mainstream scenario tree-based techniques, we reduce computational complexity by restricting the space o ...

2013