Learning vector autoregressive models with focalised Granger-causality graphs. - Magda Gregorova; Alexandros Kalousis; Stéphane Marchand-Maillet; Jun Wang 017 (CoRR, 2015)

Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks. - Magda Gregorová; Alexandros Kalousis; Stéphane Marchand-Maillet (ACML, 2017)

Forecasting and Granger Modelling with Non-linear Dynamical Dependencies. - Magda Gregorová; Alexandros Kalousis; Stéphane Marchand-Maillet (ECML/PKDD (2), 2017)

Large-Scale Nonlinear Variable Selection via Kernel Random Features. - Magda Gregorová; Jason Ramapuram; Alexandros Kalousis; Stéphane Marchand-Maillet (ECML/PKDD (2), 2018)

Structured nonlinear variable selection. - Magda Gregorova; Alexandros Kalousis; Stéphane Marchand-Maillet (UAI, 2018)

Continual Classification Learning Using Generative Models. - Frantzeska Lavda; Jason Ramapuram; Magda Gregorova; Alexandros Kalousis (CoRR, 2018)

Sparse Learning for Variable Selection with Structures and Nonlinearities. - Magda Gregorova (CoRR, 2019)

Improving VAE Generations of Multimodal Data Through Data-Dependent Conditional Priors. - Frantzeska Lavda; Magda Gregorová; Alexandros Kalousis (ECAI, 2020)

Data-Dependent Conditional Priors for Unsupervised Learning of Multimodal Data. - Frantzeska Lavda; Magda Gregorová; Alexandros Kalousis (Entropy, 2020)

Lifelong generative modeling. - Jason Ramapuram; Magda Gregorova; Alexandros Kalousis (Neurocomputing, 2020)

Learned transform compression with optimized entropy encoding. - Magda Gregorová; Marc Desaules; Alexandros Kalousis (CoRR, 2021)

Permutation Equivariant Generative Adversarial Networks for Graphs. - Yoann Boget; Magda Gregorova; Alexandros Kalousis (CoRR, 2021)

Graph annotation generative adversarial networks. - Yoann Boget; Magda Gregorova; Alexandros Kalousis (ACML, 2022)

GrannGAN: Graph annotation generative adversarial networks. - Yoann Boget; Magda Gregorova; Alexandros Kalousis (CoRR, 2022)

Diffusion-Based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation. - Philipp Väth; Alexander M. Frühwald; Benjamin Paassen; Magda Gregorova (PKDD/ECML Workshops (3), 2023)

Vector-Quantized Graph Auto-Encoder. - Yoann Boget; Magda Gregorova; Alexandros Kalousis (CoRR, 2023)

An Investigative Study Exploring Machine Learning Approaches for Optimizing Deep Brain Stimulation Programming. - Esther Ademola; Martin Reich; Magda Gregorova (MDIS, 2024)

Variational Autoencoder-Based Multiobjective Topology Optimization of Electrical Machines Using Vector Graphics. - Michael Heroth; Helmut C. Schmid; Magda Gregorová; Rainer Herrler; Wilfried Hofmann (IEEE Access, 2024)

GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling. - Philipp Väth; Alexander M. Frühwald; Benjamin Paassen; Magda Gregorova (CoRR, 2024)

Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability. - Philipp Väth; Alexander M. Frühwald; Benjamin Paassen; Magda Gregorova (CoRR, 2024)

Discrete Graph Auto-Encoder. - Yoann Boget; Magda Gregorova; Alexandros Kalousis (Trans. Mach. Learn. Res., 2024)

Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives. - Marius Benkert; Michael Heroth; Rainer Herrler; Magda Gregorová; Helmut C. Schmid (Auton. Intell. Syst., 2024)

Diffusion Classifier Guidance for Non-robust Classifiers. - Philipp Väth; Dibyanshu Kumar; Benjamin Paassen; Magda Gregorová (CoRR, 2025)

Loss Functions in Diffusion Models: A Comparative Study. - Dibyanshu Kumar; Philipp Väth; Magda Gregorová (CoRR, 2025)

Sugar-Beet Stress Detection using Satellite Image Time Series. - Bhumika Laxman Sadbhave; Philipp Väth; Denise Dejon; Gunther Schorcht; Magda Gregorová (CoRR, 2025)