Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation. - Sandra Obermeier; Max Berrendorf; Peer Kröger (WI/IAT, 2020)

Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering. - Sandra Obermeier; Anna Beer 001; Florian Wahl; Thomas Seidl 001 (BTW, 2021)

Diversity Aware Relevance Learning for Argument Search. - Michael Fromm 001; Max Berrendorf; Sandra Obermeier; Thomas Seidl 001; Evgeniy Faerman (ECIR (2), 2021)

VERIPS: Verified Pseudo-label Selection for Deep Active Learning. - Sandra Gilhuber; Philipp Jahn 001; Yunpu Ma; Thomas Seidl 001 (ICDM, 2022)

Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations. - Sandra Gilhuber; Max Berrendorf; Yunpu Ma; Thomas Seidl 001 (IAL@PKDD/ECML, 2022)

DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. - Sandra Gilhuber; Julian Busch; Daniel Rotthues; Christian M. M. Frey; Thomas Seidl 001 (ECML/PKDD (1), 2023)

How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. - Sandra Gilhuber; Rasmus Hvingelby; Mang Ling Ada Fok; Thomas Seidl 001 (ECML/PKDD (2), 2023)

FALCUN: A Simple and Efficient Deep Active Learning Strategy. - Sandra Gilhuber; Anna Beer 001; Yunpu Ma; Thomas Seidl 001 (ECML/PKDD (3), 2024)

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. - Valentin Margraf; Marcel Wever; Sandra Gilhuber; Gabriel Marques Tavares; Thomas Seidl 001; Eyke Hüllermeier (CoRR, 2024)