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Konstantinos D. Polyzos

5 accepted papers

2025

Efficient 3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning

Athanasios Bacharis, Konstantinos D. Polyzos, Henry J. Nelson, Georgios B. Giannakis, Nikolaos Papanikolopoulos
RA-L 2025

3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. While this task can be carried out using a large number of arbitrarily taken 2D images, their processi

Cited by 1Source ↗Scholar ↗
2023

Bayesian Optimization with Ensemble Learning Models and Adaptive Expected Improvement

Konstantinos D. Polyzos, Qin Lu, Georgios B. Giannakis
ICASSP 2023accepted

Optimizing a black-box function that is expensive to evaluate emerges in a gamut of machine learning and artificial intelligence applications including drug discovery, policy optimization in robotics, and hyperparameter tuning of learning models to list a few. Bayesian optimization (BO) provides a p…

Cited by 0Source ↗Scholar ↗
2023

Physics-Informed Transfer Learning for Voltage Stability Margin Prediction

Manish Kumar Singh, Konstantinos D. Polyzos, Panagiotis A. Traganitis, Sairaj V. Dhople, Georgios B. Giannakis
ICASSP 2023accepted

Assessing set-membership and evaluating distances to the related set boundary are problems of widespread interest, and can often be computationally challenging. Seeking efficient learning models for such tasks, this paper deals with voltage stability margin prediction for power systems. Supervised t…

Cited by 0Source ↗Scholar ↗
2021

Graph-Adaptive Incremental Learning Using an Ensemble of Gaussian Process Experts

Konstantinos D. Polyzos, Qin Lu, Georgios B. Giannakis
ICASSP 2021accepted

Graph-guided semi-supervised learning (SSL) is a major task emerging in a gamut of network science applications. However, most SSL approaches rely on deterministic similarity metrics for prediction, thus providing only point estimates of the sought function. To allow for uncertainty quantification,…

Cited by 0Source ↗Scholar ↗