12 papers
KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models
Boshko Koloski, Xiangjian Jiang, Senja Pollak +3
Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks. Yet in niche domains, where data…
Tabular Foundation Model for Generative Modelling
Xiangjian Jiang, Mingxuan Liu, Nikola Simidjievski +2
Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, rather than optimisation for…
TabStruct: Measuring Structural Fidelity of Tabular Data
Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik
Evaluating tabular generators remains a challenging problem, as the unique causal structural prior of heterogeneous tabular data does not lend itself to intuitive human inspection.…
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Konstantin Hemker, Andrew H. Song, Cristina Almagro-Pérez +6
Spatial transcriptomics (ST) provides spatially resolved measurements of gene expression, enabling characterization of the molecular landscape of human tissue beyond histological a…
Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine
Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
Learning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities…
RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data
Urška Matjašec, Nikola Simidjievski, Mateja Jamnik
Tree-based models are often robust to uninformative features and can accurately capture non-smooth, complex decision boundaries. Consequently, they often outperform neural network-…