George V. Moustakides

is this you? claim profile

0 followers
Rutgers University

Query-Based Selection of Optimal Candidates under the Mallows Model

We introduce the problem of query-based selection of the optimal candida...
01/18/2021 ∙ by Xujun Liu, et al. ∙ 0 ∙ share

read it
Quickest Detection of Moving Anomalies in Sensor Networks

The problem of sequentially detecting a moving anomaly which affects dif...
07/28/2020 ∙ by Georgios Rovatsos, et al. ∙ 0 ∙ share

read it
Image De-Quantization Using Generative Models as Priors

Image quantization is used in several applications aiming in reducing th...
07/15/2020 ∙ by Kalliopi Basioti, et al. ∙ 0 ∙ share

read it
Image Restoration from Parametric Transformations using Generative Models

When images are statistically described by a generative model we can use...
05/27/2020 ∙ by Kalliopi Basioti, et al. ∙ 7 ∙ share

read it
Designing GANs: A Likelihood Ratio Approach

We are interested in the design of generative adversarial networks. The ...
02/03/2020 ∙ by Kalliopi Basioti, et al. ∙ 44 ∙ share

read it
Training Neural Networks for Likelihood/Density Ratio Estimation

Various problems in Engineering and Statistics require the computation o...
11/01/2019 ∙ by George V. Moustakides, et al. ∙ 0 ∙ share

read it
Optimizing Shallow Networks for Binary Classification

Data driven classification that relies on neural networks is based on op...
05/24/2019 ∙ by Kalliopi Basioti, et al. ∙ 0 ∙ share

read it
Detecting Changes in Hidden Markov Models

We consider the problem of sequential detection of a change in the stati...
01/24/2019 ∙ by George V. Moustakides, et al. ∙ 0 ∙ share

read it
Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors

We consider the sequential change-point detection for asynchronous multi...
01/24/2019 ∙ by Liyan Xie, et al. ∙ 0 ∙ share

read it
Kernel-Based Training of Generative Networks

Generative adversarial networks (GANs) are designed with the help of min...
11/23/2018 ∙ by Kalliopi Basioti, et al. ∙ 4 ∙ share

read it
Sequential Subspace Changepoint Detection

We consider the sequential changepoint detection problem of detecting ch...
11/09/2018 ∙ by Liyan Xie, et al. ∙ 0 ∙ share

read it
First-order optimal sequential subspace change-point detection

We consider the sequential change-point detection problem of detecting c...
06/28/2018 ∙ by Liyan Xie, et al. ∙ 0 ∙ share

read it
Optimal Stopping for Interval Estimation in Bernoulli Trials

We propose an optimal sequential methodology for obtaining confidence in...
11/18/2017 ∙ by Tony Yaacoub, et al. ∙ 0 ∙ share

read it

George V. Moustakides

Featured Co-authors

Yao Xie

Olgica Milenkovic

Venugopal V. Veeravalli

Liyan Xie

Yajun Mei

Kalliopi Basioti

Xujun Liu

Georgios Rovatsos

Tony Yaacoub

Emmanouil Z. Psarakis

Query-Based Selection of Optimal Candidates under the Mallows Model

Quickest Detection of Moving Anomalies in Sensor Networks

Image De-Quantization Using Generative Models as Priors

Image Restoration from Parametric Transformations using Generative Models

Designing GANs: A Likelihood Ratio Approach

Training Neural Networks for Likelihood/Density Ratio Estimation

Optimizing Shallow Networks for Binary Classification

Detecting Changes in Hidden Markov Models

Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors

Kernel-Based Training of Generative Networks

Sequential Subspace Changepoint Detection

First-order optimal sequential subspace change-point detection

Optimal Stopping for Interval Estimation in Bernoulli Trials