Publications (10)
DALLMi: Domain Adaption for LLM-based Multi-label Classifier
Miruna BeÅ£ianu, Abele MÄlan, Marco Aldinucci +2
Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering…
TabuLa: Harnessing Language Models for Tabular Data Synthesis
Zilong Zhao, Robert Birke, Lydia Chen
Tabular data synthesis is crucial for addressing privacy and security concerns in industries reliant on tabular data. While recent advancements adopt large language models (LLMs) f…
CCBNet: Confidential Collaborative Bayesian Networks Inference
Abele MÄlan, Jérémie Decouchant, Thiago Guzella +1
Effective large-scale process optimization in manufacturing industries requires close cooperation between different human expert parties who encode their knowledge of related domai…
A Unified Seeding Framework
Ya-Wen Teng, Hsi-Wen Chen, De-Nian Yang +3
Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds…
Targeted Influence with Community and Gender-Aware Seeding
Maciej Styczen, Bing-Jyue Chen, Ya-Wen Teng +3
When spreading information over social networks, seeding algorithms selecting users to start the dissemination play a crucial role. The majority of existing seeding algorithms focu…
DTGAN: Differential Private Training for Tabular GANs
Aditya Kunar, Robert Birke, Zilong Zhao +1
Tabular generative adversarial networks (TGAN) have recently emerged to cater to the need of synthesizing tabular data -- the most widely used data format. While synthetic tabular…