1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks
Xiangjian Jiang, Yanyi Pu
Dynamic Graph Neural Networks (DGNNs) have emerged as the predominant approach for processing dynamic graph-structured data. However, the influence of temporal information on model…