1 citations · 2 across the 3 of their papers we have counts for
5 papers
Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability
Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer +6
Relational in-context learning (ICL) uses labeled support examples and their linked relational context to predict labels for new queries. This creates a failure mode when target-de…
Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Impact, Detection, and Mitigation
Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer +5
Relational in-context learning (ICL) conditions predictions on the labeled support examples and their linked tables, creating a failure mode when the support set contains target-de…
Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning
Divyansha Lachi, Mahmoud Mohammadi, Joe Meyer +3
In domains such as healthcare, finance, and e-commerce, the temporal dynamics of relational data emerge from complex interactions-such as those between patients and providers, or u…
RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphs
Joe Meyer, Divyansha Lachi, Mahmoud Mohammadi +4
Relational multi-table data is common in domains such as e-commerce, healthcare, and scientific research, and can be naturally represented as heterogeneous temporal graphs with mul…
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…