1 citations · 4 across the 6 of their papers we have counts for
6 papers
KumoRFM-2: Scaling Foundation Models for Relational Learning
Valter Hudovernik, Federico López, Vid Kocijan +4
We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fine-tuning and is applicable to…
RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
Justin Gu, Rishabh Ranjan, Charilaos Kanatsoulis +8
Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interco…
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train…
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Rishabh Ranjan, Valter Hudovernik, Mark Znidar +7
Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and task…
RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models
Valter Hudovernik, Minkai Xu, Juntong Shi +4
Real-world databases are predominantly relational, comprising multiple interlinked tables that contain complex structural and statistical dependencies. Learning generative models o…
Benchmarking the Fidelity and Utility of Synthetic Relational Data
Valter Hudovernik, Martin Jurkovič, Erik Štrumbelj
Synthesizing relational data has started to receive more attention from researchers, practitioners, and industry. The task is more difficult than synthesizing a single table due to…