Sandra Gilhuber

According to our database, Sandra Gilhuber authored at least 9 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data.

Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier

CoRR, 2024

FALCUN: A Simple and Efficient Deep Active Learning Strategy.

Sandra Gilhuber, Anna Beer, Yunpu Ma, Thomas Seidl

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

2023

How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning.

Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification.

Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations.

Sandra Gilhuber, Max Berrendorf, Yunpu Ma, Thomas Seidl

Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), 2022

VERIPS: Verified Pseudo-label Selection for Deep Active Learning.

Sandra Gilhuber, Philipp Jahn, Yunpu Ma, Thomas Seidl

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

Diversity Aware Relevance Learning for Argument Search.

Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman

Proceedings of the Advances in Information Retrieval, 2021

Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering.

Sandra Obermeier, Anna Beer, Florian Wahl, Thomas Seidl

Proceedings of the Datenbanksysteme für Business, 2021

2020

Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation.

Sandra Obermeier, Max Berrendorf, Peer Kröger

Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2020