6 papers
PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining
Mohammad Sadeq Abolhasani, Viswanath Ganapathy
Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model traini…
Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data
Mohammad Sadeq Abolhasani, Viswanath Ganapathy
Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on millions of synthetic tasks. We…
Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation
Mohammad Sadeq Abolhasani, Yang Ba, Yixuan He +1
Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly sch…
Measuring Dataset Diversity from a Geometric Perspective
Yang Ba, Mohammad Sadeq Abolhasani, Michelle V Mancenido +1
Diversity can be broadly defined as the presence of meaningful variation across elements, which can be viewed from multiple perspectives, including statistical variation and geomet…
Predict Training Data Quality via Its Geometry in Metric Space
Yang Ba, Mohammad Sadeq Abolhasani, Rong Pan
High-quality training data is the foundation of machine learning and artificial intelligence, shaping how models learn and perform. Although much is known about what types of data…
Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation
Mohammad Sadeq Abolhasani, Rong Pan
Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors.…