Feature Learning for Enhanced Security in the Internet of Things. - Enrico Mattei; Cass Dalton; Andrew Draganov; Brent Marin; Michael Tinston; Greg Harrison; Bob Smarrelli; Marc Harlacher (GlobalSIP, 2019)

Open set recognition through unsupervised and class-distance learning. - Andrew Draganov; Carter Brown; Enrico Mattei; Cass Dalton; Jaspreet Ranjit (WiseML@WiSec, 2020)

ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting. - Carter N. Brown; Enrico Mattei; Andrew Draganov (CoRR, 2021)

GiDR-DUN; Gradient Dimensionality Reduction - Differences and Unification. - Andrew Draganov; Tyrus Berry; Jakob Rødsgaard Jørgensen; Katrine Scheel Nellemann; Ira Assent; Davide Mottin (CoRR, 2022)

ActUp: Analyzing and Consolidating tSNE and UMAP. - Andrew Draganov; Jakob Rødsgaard Jørgensen; Katrine Scheel; Davide Mottin; Ira Assent; Tyrus Berry; Çigdem Aslay (IJCAI, 2023)

Connecting the Dots - Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering. - Anna Beer 001; Andrew Draganov; Ellen Hohma; Philipp Jahn 001; Christian M. M. Frey; Ira Assent (KDD, 2023)

Unexplainable Explanations: Towards Interpreting tSNE and UMAP Embeddings. - Andrew Draganov; Simon Dohn (CoRR, 2023)

UCoDe: unified community detection with graph convolutional networks. - Atefeh Moradan; Andrew Draganov; Davide Mottin; Ira Assent (Mach. Learn., 2023)

DROPP: Structure-Aware PCA for Ordered Data: A General Method and its Applications in Climate Research and Molecular Dynamics. - Anna Beer 001; Olivér Palotás; Andrea Maldonado 001; Andrew Draganov; Ira Assent (ICDE, 2024)

The Hidden Pitfalls of the Cosine Similarity Loss. - Andrew Draganov; Sharvaree P. Vadgama; Erik J. Bekkers (CoRR, 2024)

Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. - Andrew Draganov; David Saulpic; Chris Schwiegelshohn (Proc. ACM Manag. Data, 2024)

A Tight VC-Dimension Analysis of Clustering Coresets with Applications. - Vincent Cohen-Addad; Andrew Draganov; Matteo Russo 002; David Saulpic; Chris Schwiegelshohn (SODA, 2025)

On the Importance of Embedding Norms in Self-Supervised Learning. - Andrew Draganov; Sharvaree P. Vadgama; Sebastian Damrich; Jan Niklas Böhm; Lucas Maes; Dmitry Kobak; Erik J. Bekkers (CoRR, 2025)

I Want 'Em All (At Once) - Ultrametric Cluster Hierarchies. - Andrew Draganov; Pascal Weber 001; Rasmus Skibdahl Melanchton Jørgensen; Anna Beer 001; Claudia Plant; Ira Assent (CoRR, 2025)

Node Embeddings via Neighbor Embeddings. - Jan Niklas Böhm; Marius Keute; Alica Guzmán; Sebastian Damrich; Andrew Draganov; Dmitry Kobak (CoRR, 2025)