5 citations · 5 across the 5 of their papers we have counts for
5 papers
What are the Right Symmetries for Formal Theorem Proving?
Krzysztof Olejniczak, Radoslav Dimitrov, Xingyue Huang +3
Formal theorem provers based on large language models (LLMs) are highly sensitive to superficial variations in problem representation: semantically equivalent statements can exhibi…
RelAgent: LLM Agents as Data Scientists for Relational Learning
Xingyue Huang, Louis Tichelman, Jinwoo Kim +2
Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, graph transformers), tabular…
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation m…
One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs
Krzysztof Olejniczak, Xingyue Huang, Mikhail Galkin +1
Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerged, where the goal is to predict answers that do not necessarily appear in the…
Text Detection Forgot About Document OCR
Krzysztof Olejniczak, Milan Šulc
Detection and recognition of text from scans and other images, commonly denoted as Optical Character Recognition (OCR), is a widely used form of automated document processing with…