Valter Hudovernik — accepted AI-conference papers

2026

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin +1

ICML 2026poster

Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train such models are rarely public due to privacy constraints. While there are methods to generate synthetic tabular data of…

Cited by 0 Source ↗Scholar ↗

2026

Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data

Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos I. Kanatsoulis, Roshan Reddy Upendra, Mahmoud Mohammadi +4

ICLR 2026poster

Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures, and fun…

Cited by 0 Source ↗code ↗Scholar ↗