6 citations · 10 across the 10 of their papers we have counts for
10 papers
SPD: Single Pass Decoding for Generative Reranking
Emil Laftchiev, Prachi Agrawal, Moe Kayali +7
Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive decoding spends one sequential fo…
SSH-Passkeys: Leveraging Web Authentication for Passwordless SSH
Moe Kayali, Jonas Schmitt, Franziska Roesner
We propose a method for using Web Authentication APIs for SSH authentication, enabling passwordless remote server login with passkeys. These are credentials that are managed throug…
Mind the Data Gap: Bridging LLMs to Enterprise Data Integration
Moe Kayali, Fabian Wenz, Nesime Tatbul +1
Leading large language models (LLMs) are trained on public data. However, most of the world's data is dark data that is not publicly accessible, mainly in the form of private organ…
QirK: Question Answering via Intermediate Representation on Knowledge Graphs
Jan Luca Scheerer, Anton Lykov, Moe Kayali +4
We demonstrate QirK, a system for answering natural language questions on Knowledge Graphs (KG). QirK can answer structurally complex questions that are still beyond the reach of e…
Making LLMs Work for Enterprise Data Tasks
Çağatay Demiralp, Fabian Wenz, Peter Baile Chen +3
Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs'…
Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation
Kyle Deeds, Diandre Sabale, Moe Kayali +1
Graph workloads pose a particularly challenging problem for query optimizers. They typically feature large queries made up of entirely many-to-many joins with complex correlations.…
CHORUS: Foundation Models for Unified Data Discovery and Exploration
Moe Kayali, Anton Lykov, Ilias Fountalis +3
We apply foundation models to data discovery and exploration tasks. Foundation models include large language models (LLMs) that show promising performance on a range of diverse tas…
Quasi-stable Coloring for Graph Compression: Approximating Max-Flow, Linear Programs, and Centrality
Moe Kayali, Dan Suciu
We propose quasi-stable coloring, an approximate version of stable coloring. Stable coloring, also called color refinement, is a well-studied technique in graph theory for classify…
Mining Robust Default Configurations for Resource-constrained AutoML
Moe Kayali, Chi Wang
Automatic machine learning (AutoML) is a key enabler of the mass deployment of the next generation of machine learning systems. A key desideratum for future ML systems is the autom…
Causal Relational Learning
Babak Salimi, Harsh Parikh, Moe Kayali +3
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in ca…