113 citations · 250 across the 24 of their papers we have counts for
6 papers · 1 filter
Approximate Inference in Structured Instances with Noisy Categorical Observations
Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
We study the problem of recovering the latent ground truth labeling of a structured instance with categorical random variables in the presence of noisy observations. We present a n…
Technical Report: Optimizing Human Involvement for Entity Matching and Consolidation
Ji Sun, Dong Deng, Ihab Ilyas +5
An end-to-end data integration system requires human feedback in several phases, including collecting training data for entity matching, debugging the resulting clusters, confirmin…
HoloDetect: Few-Shot Learning for Error Detection
Alireza Heidari, Joshua McGrath, Ihab F. Ilyas +1
We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detectio…
Properties of Inconsistency Measures for Databases
Ester Livshits, Rina Kochirgan, Segev Tsur +3
How should we quantify the inconsistency of a database that violates integrity constraints? Proper measures are important for various tasks, such as progress indication and action…
Matching Entities Across Different Knowledge Graphs with Graph Embeddings
Michael Azmy, Peng Shi, Jimmy Lin +1
This paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world en…
Distributed Dependency Discovery
Hemant Saxena, Lukasz Golab, Ihab F. Ilyas
We analyze the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of p…