Efficient and Flexible Inference for Stochastic Systems. - Stefan Bauer; Nico S. Gorbach; Ðorðe Miladinovic; Joachim M. Buhmann (NIPS, 2017)

Interventional Robustness of Deep Latent Variable Models. - Raphael Suter; Ðorðe Miladinovic; Stefan Bauer; Bernhard Schölkopf (CoRR, 2018)

Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks. - Patrick Schwab; Djordje Miladinovic; Walter Karlen (AAAI, 2019)

Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments. - Ðorðe Miladinovic; Muhammad Waleed Gondal; Bernhard Schölkopf; Joachim M. Buhmann; Stefan Bauer (DGS@ICLR, 2019)

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness. - Raphael Suter; Ðorðe Miladinovic; Bernhard Schölkopf; Stefan Bauer (ICML, 2019)

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. - Muhammad Waleed Gondal; Manuel Wuthrich; Djordje Miladinovic; Francesco Locatello; Martin Breidt; Valentin Volchkov; Joel Akpo; Olivier Bachem; Bernhard Schölkopf; Stefan Bauer (NeurIPS, 2019)

Disentangled State Space Representations. - Ðorðe Miladinovic; Muhammad Waleed Gondal; Bernhard Schölkopf; Joachim M. Buhmann; Stefan Bauer (CoRR, 2019)

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. - Ðorðe Miladinovic; Christine Muheim; Stefan Bauer; Andrea Spinnler; Daniela Noain; Mojtaba Bandarabadi; Benjamin Gallusser; Gabriel Krummenacher; Christian R. Baumann; Antoine Adamantidis; Steven A. Brown; Joachim M. Buhmann (PLoS Comput. Biol., 2019)

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. - Ðorðe Miladinovic; Aleksandar Stanic; Stefan Bauer; Jürgen Schmidhuber; Joachim M. Buhmann (ICLR, 2021)

Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation. - João B. S. Carvalho; João A. Santinha; Djordje Miladinovic; Joachim M. Buhmann (CoRR, 2021)

On Training Deep Generative Models with Latent Variables. - Djordje Miladinovic (2021)

Holistic Modeling In Medical Image Segmentation Using Spatial Recurrence. - João B. S. Carvalho; João A. Santinha; Djordje Miladinovic; Carlos Cotrini; Joachim M. Buhmann (MIDL, 2022)

Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs. - Djordje Miladinovic; Kumar Shridhar; Kushal Jain; Max B. Paulus; Joachim M. Buhmann; Carl Allen (NeurIPS, 2022)

In-silico biological discovery with large perturbation models. - Djordje Miladinovic; Tobias Höppe; Mathieu Chevalley; Andreas Georgiou; Lachlan Stuart; Arash Mehrjou; Marcus Bantscheff; Bernhard Schölkopf; Patrick Schwab (CoRR, 2025)