Introducing Noise in Decentralized Training of Neural Networks. - Linara Adilova; Nathalie Paul; Peter Schlicht (DMLE/IOTSTREAMING@PKDD/ECML, 2018)

Efficient Decentralized Deep Learning by Dynamic Model Averaging. - Michael Kamp; Linara Adilova; Joachim Sicking; Fabian Hüger; Peter Schlicht; Tim Wirtz; Stefan Wrobel (ECML/PKDD (1), 2018)

Making Efficient Use of a Domain Expert's Time in Relation Extraction. - Linara Adilova; Sven Giesselbach; Stefan Rüping 001 (CoRR, 2018)

System Misuse Detection Via Informed Behavior Clustering and Modeling. - Linara Adilova; Livin Natious; Siming Chen 001; Olivier Thonnard; Michael Kamp (DSN Workshops, 2019)

Information-Theoretic Perspective of Federated Learning. - Linara Adilova; Julia Rosenzweig; Michael Kamp (CoRR, 2019)

A Reparameterization-Invariant Flatness Measure for Deep Neural Networks. - Henning Petzka; Linara Adilova; Michael Kamp; Cristian Sminchisescu (CoRR, 2019)

Resource-Constrained On-Device Learning by Dynamic Averaging. - Lukas Heppe; Michael Kamp; Linara Adilova; Danny Heinrich; Nico Piatkowski; Katharina Morik (PKDD/ECML Workshops, 2020)

Feature-Robustness, Flatness and Generalization Error for Deep Neural Networks. - Henning Petzka; Linara Adilova; Michael Kamp; Cristian Sminchisescu (CoRR, 2020)

LDA Ensembles for Interactive Exploration and Categorization of Behaviors. - Siming Chen 001; Natalia V. Andrienko; Gennady L. Andrienko; Linara Adilova; Jérémie Barlet; Jörg Kindermann; Phong H. Nguyen; Olivier Thonnard; Cagatay Turkay (IEEE Trans. Vis. Comput. Graph., 2020)

Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation. - Linara Adilova; Elena Schulz; Maram Akila; Sebastian Houben; Jan David Schneider; Fabian Hüger; Tim Wirtz (CVPR Workshops, 2021)

Relative Flatness and Generalization. - Henning Petzka; Michael Kamp; Linara Adilova; Cristian Sminchisescu; Mario Boley (NeurIPS, 2021)

Approaches to Uncertainty Quantification in Federated Deep Learning. - Florian Linsner; Linara Adilova; Sina Däubener; Michael Kamp; Asja Fischer (PKDD/ECML Workshops (1), 2021)

Novelty Detection in Sequential Data by Informed Clustering and Modeling. - Linara Adilova; Siming Chen 001; Michael Kamp (CoRR, 2021)

Visual Analytics for Human-Centered Machine Learning. - Natalia V. Andrienko; Gennady L. Andrienko; Linara Adilova; Stefan Wrobel; Theresa-Marie Rhyne (IEEE Computer Graphics and Applications, 2022)

Information Plane Analysis for Dropout Neural Networks. - Linara Adilova; Bernhard C. Geiger; Asja Fischer (ICLR, 2023)

FAM: Relative Flatness Aware Minimization. - Linara Adilova; Amr Abourayya; Jianning Li 002; Amin Dada; Henning Petzka; Jan Egger; Jens Kleesiek; Michael Kamp (TAG-ML, 2023)

Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog. - Maximilian Poretschkin; Anna Schmitz 001; Maram Akila; Linara Adilova; Daniel Becker 010; Armin B. Cremers; Dirk Hecker; Sebastian Houben; Michael Mock; Julia Rosenzweig; Joachim Sicking; Elena Schulz; et al. (CoRR, 2023)

Layerwise Linear Mode Connectivity. - Linara Adilova; Asja Fischer; Martin Jaggi (CoRR, 2023)

Layer-wise linear mode connectivity. - Linara Adilova; Maksym Andriushchenko; Michael Kamp; Asja Fischer; Martin Jaggi (ICLR, 2024)

The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective. - Nils Philipp Walter; Linara Adilova; Jilles Vreeken; Michael Kamp (CoRR, 2024)

Landscaping Linear Mode Connectivity. - Sidak Pal Singh; Linara Adilova; Michael Kamp; Asja Fischer; Bernhard Schölkopf; Thomas Hofmann (CoRR, 2024)

Re-interpreting rules interpretability. - Linara Adilova; Michael Kamp; Gennady L. Andrienko; Natalia V. Andrienko (Int. J. Data Sci. Anal., 2025)