Learning from Cloud latency measurements. - Pavol Mulinka; Lukas Kencl (ICC Workshops, 2015)

Multidimensional cloud latency monitoring and evaluation. - Ondrej Tomanek; Pavol Mulinka; Lukas Kencl (Comput. Networks, 2016)

Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering. - Pavol Mulinka; Pedro Casas; Lukas Kencl (CloudNet, 2018)

Stream-based Machine Learning for Network Security and Anomaly Detection. - Pavol Mulinka; Pedro Casas (Big-DAMA@SIGCOMM, 2018)

Adaptive Network Security through Stream Machine Learning. - Pavol Mulinka; Pedro Casas (SIGCOMM Posters and Demos, 2018)

Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks. - Pedro Casas; Pavol Mulinka; Juan Martin Vanerio (MTD@CCS, 2019)

Continuous and Adaptive Learning over Big Streaming Data for Network Security. - Pavol Mulinka; Pedro Casas; Juan Martin Vanerio (CloudNet, 2019)

ADAM & RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring. - Sarah Wassermann; Thibaut Cuvelier; Pavol Mulinka; Pedro Casas (CNSM, 2019)

WhatsThat? On the Usage of Hierarchical Clustering for Unsupervised Detection & Interpretation of Network Attacks. - Pavol Mulinka; Kensuke Fukuda; Pedro Casas; Lukas Kencl (EuroS&P Workshops, 2020)

HUMAN - Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation. - Pavol Mulinka; Pedro Casas; Kensuke Fukuda; Lukas Kencl (NOF, 2020)

Information Processing and Data Visualization in Networked Industrial Systems. - Pavol Mulinka; Charalampos Kalalas; Merim Dzaferagic; Irene Macaluso; Daniel Gutierrez-Rojas; Pedro Henrique Juliano Nardelli; Nicola Marchetti (PIMRC, 2021)

Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis. - Sarah Wassermann; Thibaut Cuvelier; Pavol Mulinka; Pedro Casas (IEEE Trans. Netw. Serv. Manag., 2021)

A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks. - Souneil Park; Pavol Mulinka; Diego Perino (COMPASS, 2022)

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics. - Alexander Beattie; Pavol Mulinka; Subham Sahoo; Ioannis T. Christou; Charalampos Kalalas; Daniel Gutierrez-Rojas; Pedro H. J. Nardelli (SmartGridComm, 2022)

Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters - A Bayesian Approach. - Pavol Mulinka; Subham Sahoo; Charalampos Kalalas; Pedro H. J. Nardelli (CoRR, 2022)

pytorch-widedeep: A flexible package for multimodal deep learning. - Javier Rodriguez Zaurin; Pavol Mulinka (J. Open Source Softw., 2023)

Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting. - Nikolaos Pavlidis; Vasileios Perifanis; Selim F. Yilmaz; Francesc Wilhelmi; Marco Miozzo; Pavlos S. Efraimidis; Remous-Aris Koutsiamanis; Pavol Mulinka; Paolo Dini (IEEE Trans. Sustain. Comput., 2025)

AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration. - Charalampos Kalalas; Pavol Mulinka; Guillermo Candela Belmonte; Miguel Fornell; Michail Dalgitsis; Francisco Paredes Vera; Javier Santaella Sánchez; Carmen Vicente Villares; Roshan Sedar; Eftychia G. Datsika; Angelos Antonopoulos 001; Antonio Fernández Ojea; et al. (CoRR, 2025)