Personal Website of George V. Moustakides
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Scientific Activities
Publications
Submitted
Teaching
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CS Rutgers
Submitted
K. Basioti, G.V. Moustakides, Neural network based binary classification, submitted to a Machine Learning conference. (Sept. 2019)
G. Fellouris, G.V. Moustakides, Communication efficient decentralized sequential change detection, submitted to the Bernoulli Journal. (Aug. 2019)
L. Xie, Y. Xie, G.V. Moustakides, Sequential subspace changepoint detection, submitted to the IEEE Transactions on Signal Processing. (July 2019)
G. Rovatsos, G.V. Moustakides, V.V. Veeravalli, Quickest detection of an anomaly in a sensor network, submitted to the 2019 Asilomar conference. (May 2019)
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Working Papers
G.V. Moustakides, K. Basioti, Density/Likelihood ratio estimation using neural networks, to be submitted to the IEEE Transactions on Information Theory.
K. Basioti, G.V. Moustakides, Training networks for multiclass classification, to be submitted to a Machine Learning conference.
G.V. Moustakides, T. Banerjee, Sequential detection of statistically periodic processes, to be submitted to the IEEE Transactions on Information Theory.
G. Rovatsos, G.V. Moustakides, V.V. Veeravalli, Dynamic anomaly detection in sensor networks, to be submitted to suitable conference and journal.
J.G. Ligo, G.V. Moustakides, V.V. Veeravalli, Detecting sparse mixtures: Rate of decay of error probability, to be submitted to the Bernoulli journal.
J.G. Ligo, G.V. Moustakides, V.V. Veeravalli, Detecting sparse mixtures via quantization: Low complexity, high-performance tests, to be submitted to the IEEE Transactions on Information Theory.
G.V. Moustakides, V.V. Veeravalli, Optimum sequential detection of transitory changes, to be submitted to the IEEE Transactions on Information Theory.
G.V. Moustakides, Sequential detection of changes with Markovian data, to be submitted to the Annals of Statistics.
G.V. Moustakides, Sequential rate-change detection in homogeneous Poisson processes, to be submitted to the Annals of Statistics.
G.V. Moustakides, Adaptive dependent source separation, to be submitted to the IEEE Transactions on Signal Processing.
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