Moe Kayali

According to our database, Moe Kayali authored at least 9 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

CHORUS: Foundation Models for Unified Data Discovery and Exploration.

Moe Kayali, Anton Lykov, Ilias Fountalis, Nikolaos Vasiloglou, Dan Olteanu, Dan Suciu

Proc. VLDB Endow., April, 2024

BEAVER: An Enterprise Benchmark for Text-to-SQL.

Peter Baile Chen, Fabian Wenz, Yi Zhang, Moe Kayali, Nesime Tatbul, Michael J. Cafarella, Çagatay Demiralp, Michael Stonebraker

CoRR, 2024

QirK: Question Answering via Intermediate Representation on Knowledge Graphs.

Jan Luca Scheerer, Anton Lykov, Moe Kayali, Ilias Fountalis, Dan Olteanu, Nikolaos Vasiloglou, Dan Suciu

CoRR, 2024

Making LLMs Work for Enterprise Data Tasks.

Çagatay Demiralp, Fabian Wenz, Peter Baile Chen, Moe Kayali, Nesime Tatbul, Michael Stonebraker

CoRR, 2024

Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation.

Kyle Deeds, Diandre Sabale, Moe Kayali, Dan Suciu

CoRR, 2024

2022

Quasi-stable Coloring for Graph Compression: Approximating Max-Flow, Linear Programs, and Centrality.

Moe Kayali, Dan Suciu

Proc. VLDB Endow., 2022

Mining Robust Default Configurations for Resource-constrained AutoML.

Moe Kayali, Chi Wang

CoRR, 2022

2020

Demonstration of Inferring Causality from Relational Databases with CaRL.

Moe Kayali, Babak Salimi, Dan Suciu

Proc. VLDB Endow., 2020

Causal Relational Learning.

Babak Salimi, Harsh Parikh, Moe Kayali, Lise Getoor, Sudeepa Roy, Dan Suciu

Proceedings of the 2020 International Conference on Management of Data, 2020