Research Paper: Semi-automated Construction of Decision Rules to Predict Morbidities from Clinical Texts. - Richárd Farkas; György Szarvas; István Hegedüs; Attila Almási; Veronika Vincze; Róbert Ormándi; Róbert Busa-Fekete (J. Am. Medical Informatics Assoc., 2009)

Novel Balanced Feature Representation for Wikipedia Vandalism Detection Task - Lab Report for PAN at CLEF 2010. - István Hegedüs; Róbert Ormándi; Richárd Farkas; Márk Jelasity (CLEF (Notebook Papers/LABs/Workshops), 2010)

Automatic free-text-tagging of online news archives. - Richárd Farkas; Gábor Berend; István Hegedüs; András Kárpáti; Balázs Krich (ECAI, 2010)

Overlay Management for Fully Distributed User-Based Collaborative Filtering. - Róbert Ormándi; István Hegedüs; Márk Jelasity (Euro-Par (1), 2010)

Opinion Mining by Transformation-Based Domain Adaptation. - Róbert Ormándi; István Hegedüs; Richárd Farkas (TSD, 2010)

Towards Inferring Ratings from User Behavior in BitTorrent Communities. - Róbert Ormándi; István Hegedüs; Kornel Csernai; Márk Jelasity (WETICE, 2010)

Asynchronous Peer-to-Peer Data Mining with Stochastic Gradient Descent. - Róbert Ormándi; István Hegedüs; Márk Jelasity (Euro-Par (1), 2011)

Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data - Róbert Ormándi; István Hegedüs; Márk Jelasity (CoRR, 2011)

Peer-to-Peer Multi-class Boosting. - István Hegedüs; Róbert Busa-Fekete; Róbert Ormándi; Márk Jelasity; Balázs Kégl (Euro-Par, 2012)

Gossip-Based Learning under Drifting Concepts in Fully Distributed Networks. - István Hegedüs; Róbert Ormándi; Márk Jelasity (SASO, 2012)

Detecting concept drift in fully distributed environments. - István Hegedüs; Lehel Nyers; Róbert Ormándi (SISY, 2012)

Gossip-based distributed stochastic bandit algorithms. - Balázs Szörényi; Róbert Busa-Fekete; István Hegedüs; Róbert Ormándi; Márk Jelasity; Balázs Kégl (ICML (3), 2013)

Massively Distributed Concept Drift Handling in Large Networks. - István Hegedüs; Róbert Ormándi; Márk Jelasity (Adv. Complex Syst., 2013)

Gossip learning with linear models on fully distributed data. - Róbert Ormándi; István Hegedüs; Márk Jelasity (Concurr. Comput. Pract. Exp., 2013)

Lightning fast asynchronous distributed k-means clustering. - Árpád Berta; István Hegedüs; Róbert Ormándi (ESANN, 2014)

Fully distributed robust singular value decomposition. - István Hegedüs; Márk Jelasity; Levente Kocsis; András A. Benczúr (P2P, 2014)

Dimension Reduction Methods for Collaborative Mobile Gossip Learning. - Árpád Berta; István Hegedüs; Márk Jelasity (PDP, 2016)

Distributed Differentially Private Stochastic Gradient Descent: An Empirical Study. - István Hegedüs; Márk Jelasity (PDP, 2016)

Robust Decentralized Differentially Private Stochastic Gradient Descent. - István Hegedüs; Árpád Berta; Márk Jelasity (J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 2016)

Robust Decentralized Low-Rank Matrix Decomposition. - István Hegedüs; Árpád Berta; Levente Kocsis; András A. Benczúr; Márk Jelasity (ACM Trans. Intell. Syst. Technol., 2016)

Differentially Private Linear Models for Gossip Learning through Data Perturbation. - István Hegedüs; Márk Jelasity (Open J. Internet Things, 2017)

Gossip-Based Machine Learning in Fully Distributed Environments - István Hegedüs (2017)

Robust Fully Distributed Minibatch Gradient Descent with Privacy Preservation. - Gábor Danner; Árpád Berta; István Hegedüs; Márk Jelasity (Secur. Commun. Networks, 2018)

Gossip Learning as a Decentralized Alternative to Federated Learning. - István Hegedüs; Gábor Danner; Márk Jelasity (DAIS, 2019)

Adversarial robustness of linear models: regularization and dimensionality. - István Megyeri; István Hegedüs; Márk Jelasity (ESANN, 2019)

Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning. - István Hegedüs; Gábor Danner; Márk Jelasity (PKDD/ECML Workshops (1), 2019)

Attacking Model Sets with Adversarial Examples. - István Megyeri; István Hegedüs; Márk Jelasity (ESANN, 2020)

Adversarial Robustness of Model Sets. - István Megyeri; István Hegedüs; Márk Jelasity (IJCNN, 2020)

Decentralized machine learning using compressed push-pull averaging. - Gábor Danner; István Hegedüs; Márk Jelasity (DICG@Middleware, 2020)

Robust Classification Combined with Robust out-of-Distribution Detection: An Empirical Analysis. - István Megyeri; István Hegedüs; Márk Jelasity (IJCNN, 2021)

Decentralized learning works: An empirical comparison of gossip learning and federated learning. - István Hegedüs; Gábor Danner; Márk Jelasity (J. Parallel Distributed Comput., 2021)

Hiding Needles in a Haystack: Towards Constructing Neural Networks that Evade Verification. - Árpád Berta; Gábor Danner; István Hegedüs; Márk Jelasity (IH&MMSec, 2022)

Improving Gossip Learning via Limited Model Merging. - Gábor Danner; István Hegedüs; Márk Jelasity (ICCCI (CCIS Volume), 2023)

Privacy-preserving Federated Learning and its application to natural language processing. - Balázs Nagy; István Hegedüs; Noémi Sándor; Balázs Egedi; Haaris Mehmood; Karthikeyan Saravanan; Gábor Lóki; Ákos Kiss 001 (Knowl. Based Syst., 2023)