Predicting long-term population dynamics with bagging and boosting of process-based models. - Nikola Simidjievski; Ljupco Todorovski; Saso Dzeroski (Expert Syst. Appl., 2015)

Learning Ensembles of Process-Based Models by Bagging of Random Library Samples. - Nikola Simidjievski; Ljupco Todorovski; Saso Dzeroski (DS, 2016)

Process-Based Modeling and Design of Dynamical Systems. - Jovan Tanevski; Nikola Simidjievski; Ljupco Todorovski; Saso Dzeroski (ECML/PKDD (3), 2017)

Decoupling approximation robustly reconstructs directed dynamical networks. - Nikola Simidjievski; Jovan Tanevski; Bernard Zenko; Zoran Levnajic; Ljupco Todorovski; Saso Dzeroski (CoRR, 2017)

Machine learning for predicting thermal power consumption of the Mars Express Spacecraft. - Matej Petkovic; Redouane Boumghar; Martin Breskvar; Saso Dzeroski; Dragi Kocev; Jurica Levatic; Luke Lucas; Aljaz Osojnik; Bernard Zenko; Nikola Simidjievski (CoRR, 2018)

Towards reusable process-based models of dynamical systems: A case study in the domain of aquatic ecosystems. - Ilin Tolovski; Ana Kostovska; Nikola Simidjievski; Ljupco Todorovski; Saso Dzeroski; Pance Panov (MIPRO, 2019)

Aiding the Task of Process-Based Modeling with ProBMoTViz. - Gjorgi Peev; Nikola Simidjievski; Saso Dzeroski (Int. J. Web Appl., 2019)

Constraining Variational Inference with Geometric Jensen-Shannon Divergence. - Jacob Deasy; Nikola Simidjievski; Pietro Lió (NeurIPS, 2020)

On Second Order Behaviour in Augmented Neural ODEs. - Alexander Norcliffe; Cristian Bodnar; Ben Day; Nikola Simidjievski; Pietro Lió (NeurIPS, 2020)

Equation Discovery for Nonlinear System Identification. - Nikola Simidjievski; Ljupco Todorovski; Jus Kocijan; Saso Dzeroski (IEEE Access, 2020)

Incorporating network based protein complex discovery into automated model construction. - Paul Scherer; Maja Trebacz; Nikola Simidjievski; Zohreh Shams; Helena Andrés-Terré; Pietro Liò; Mateja Jamnik (CoRR, 2020)

Using ontology embeddings for structural inductive bias in gene expression data analysis. - Maja Trebacz; Zohreh Shams; Mateja Jamnik; Paul Scherer; Nikola Simidjievski; Helena Andrés-Terré; Pietro Liò (CoRR, 2020)

Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training. - Andrei Margeloiu; Nikola Simidjievski; Mateja Jamnik; Adrian Weller (CoRR, 2020)

Fuzzy Jaccard Index: A robust comparison of ordered lists. - Matej Petkovic; Blaz Skrlj; Dragi Kocev; Nikola Simidjievski (Appl. Soft Comput., 2021)

Attentional meta-learners are polythetic classifiers. - Ben Day; Ramón Viñas 001; Nikola Simidjievski; Pietro Liò (CoRR, 2021)

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data. - Ana Kostovska; Matej Petkovic; Tomaz Stepisnik; Luke Lucas; Timothy Finn; José Antonio Martínez Heras; Pance Panov; Saso Dzeroski; Alessandro Donati; Nikola Simidjievski; Dragi Kocev (CoRR, 2021)

Discovering outliers in the Mars Express thermal power consumption patterns. - Matej Petkovic; Luke Lucas; Tomaz Stepisnik; Pance Panov; Nikola Simidjievski; Dragi Kocev (CoRR, 2021)

Heavy-tailed denoising score matching. - Jacob Deasy; Nikola Simidjievski; Pietro Liò (CoRR, 2021)

Attentional Meta-learners for Few-shot Polythetic Classification. - Ben J. Day; Ramón Viñas Torné 001; Nikola Simidjievski; Pietro Lió (ICML, 2022)

Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. - Paul Scherer; Maja Trebacz; Nikola Simidjievski; Ramón Viñas 001; Zohreh Shams; Helena Andrés-Terré; Mateja Jamnik; Pietro Liò (Bioinform., 2022)

AiTLAS: Artificial Intelligence Toolbox for Earth Observation. - Ivica Dimitrovski; Ivan Kitanovski; Pance Panov; Nikola Simidjievski; Dragi Kocev (CoRR, 2022)

Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification. - Ivica Dimitrovski; Ivan Kitanovski; Dragi Kocev; Nikola Simidjievski (CoRR, 2022)

Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021. - Dragi Kocev; Nikola Simidjievski; Ana Kostovska; Ivica Dimitrovski; Ziga Kokalj (CoRR, 2022)

Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data. - Andrei Margeloiu; Nikola Simidjievski; Pietro Lio'; Mateja Jamnik (CoRR, 2022)

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data. - Andrei Margeloiu; Nikola Simidjievski; Pietro Liò; Mateja Jamnik (AAAI, 2023)

ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data. - Xiangjian Jiang; Andrei Margeloiu; Nikola Simidjievski; Mateja Jamnik (CoRR, 2023)

Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs. - Navindu Leelarathna; Andrei Margeloiu; Mateja Jamnik; Nikola Simidjievski (CoRR, 2023)

SHARCS: Shared Concept Space for Explainable Multimodal Learning. - Gabriele Dominici; Pietro Barbiero; Lucie Charlotte Magister; Pietro Liò; Nikola Simidjievski (CoRR, 2023)

In-Domain Self-Supervised Learning Can Lead to Improvements in Remote Sensing Image Classification. - Ivica Dimitrovski; Ivan Kitanovski; Nikola Simidjievski; Dragi Kocev (CoRR, 2023)

HEALNet - Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data. - Konstantin Hemker; Nikola Simidjievski; Mateja Jamnik (CoRR, 2023)

Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts. - Jonas Jürß; Lucie Charlotte Magister; Pietro Barbiero; Pietro Liò; Nikola Simidjievski (CoRR, 2023)

AiTLAS: Artificial Intelligence Toolbox for Earth Observation. - Ivica Dimitrovski; Ivan Kitanovski; Pance Panov; Ana Kostovska; Nikola Simidjievski; Dragi Kocev (Remote. Sens., 2023)

ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data. - Xiangjian Jiang; Andrei Margeloiu; Nikola Simidjievski; Mateja Jamnik (ICML, 2024)

HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data. - Konstantin Hemker; Nikola Simidjievski; Mateja Jamnik (NeurIPS, 2024)

TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models. - Andrei Margeloiu; Xiangjian Jiang; Nikola Simidjievski; Mateja Jamnik (NeurIPS, 2024)

MM-Lego: Modular Biomedical Multimodal Models with Minimal Fine-Tuning. - Konstantin Hemker; Nikola Simidjievski; Mateja Jamnik (CoRR, 2024)

TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting. - Andrei Margeloiu; Adrián Bazaga; Nikola Simidjievski; Pietro Liò; Mateja Jamnik (CoRR, 2024)

PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis. - Zak Buzzard; Konstantin Hemker; Nikola Simidjievski; Mateja Jamnik (CoRR, 2024)

In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification. - Ivica Dimitrovski; Ivan Kitanovski; Nikola Simidjievski; Dragi Kocev (IEEE Geosci. Remote. Sens. Lett., 2024)

GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data. - Andrei Margeloiu; Nikola Simidjievski; Pietro Lio; Mateja Jamnik (Trans. Mach. Learn. Res., 2024)

Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. - Alice Geminiani; Judith Kathrein; Alper Yegenoglu; Franziska Vogel; Marcelo Armendáriz; Ziv Ben-Zion; Petrut Antoniu Bogdan; Joana Covelo; Marissa Díaz Pier; Karin Grasenick; Vitali Karasenko; Wouter Klijn; et al. (Neuroinformatics, 2024)

Measuring Cross-Modal Interactions in Multimodal Models. - Laura Wenderoth; Konstantin Hemker; Nikola Simidjievski; Mateja Jamnik (AAAI, 2025)

Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine. - Konstantin Hemker; Nikola Simidjievski; Mateja Jamnik (ICLR, 2025)

LLM Embeddings for Deep Learning on Tabular Data. - Boshko Koloski; Andrei Margeloiu; Xiangjian Jiang; Blaz Skrlj; Nikola Simidjievski; Mateja Jamnik (CoRR, 2025)

How Well Does Your Tabular Generator Learn the Structure of Tabular Data? - Xiangjian Jiang; Nikola Simidjievski; Mateja Jamnik (CoRR, 2025)

RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data. - Urska Matjasec; Nikola Simidjievski; Mateja Jamnik (CoRR, 2025)

TabStruct: Measuring Structural Fidelity of Tabular Data. - Xiangjian Jiang; Nikola Simidjievski; Mateja Jamnik (CoRR, 2025)