3 citations · 5 across the 8 of their papers we have counts for
8 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.…
How Well Does Your Tabular Generator Learn the Structure of Tabular Data?
Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik
Heterogeneous tabular data poses unique challenges in generative modelling due to its fundamentally different underlying data structure compared to homogeneous modalities, such as…
LLM Embeddings for Deep Learning on Tabular Data
Boshko Koloski, Andrei Margeloiu, Xiangjian Jiang +3
Tabular deep-learning methods require embedding numerical and categorical input features into high-dimensional spaces before processing them. Existing methods deal with this hetero…
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
Andrei Margeloiu, Xiangjian Jiang, Nikola Simidjievski +1
Data collection is often difficult in critical fields such as medicine, physics, and chemistry. As a result, classification methods usually perform poorly with these small datasets…