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20202026
most citedOn Explaining Confounding Bias

2 citations · 3 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.DB2026

Local Stability of Rankings

Felix S. Campbell, Yuval Moskovitch

Rankings play a crucial role in decision-making. However, if minor changes to items significantly alter their rankings, the quality of the decisions being made can be compromised.…

cs.DB2026

A universal LLM Framework for General Query Refinements

Eldar Hacohen, Yuval Moskovitch, Amit Somech

Numerous studies have explored the SQL query refinement problem, where the objective is to minimally modify an input query so that it satisfies a specified set of constraints. Howe…

cs.DB2025

Causal Explanations for Disparate Trends: Where and Why?

Tal Blau, Brit Youngmann, Anna Fariha +1

During data analysis, we are often perplexed by certain disparities observed between two groups of interest within a dataset. To better understand an observed disparity, we need ex…

cs.DB20222 cited

On Explaining Confounding Bias

Brit Youngmann, Michael Cafarella, Yuval Moskovitch +1

When analyzing large datasets, analysts are often interested in the explanations for surprising or unexpected results produced by their queries. In this work, we focus on aggregate…

cs.DB2021

On Optimizing the Trade-off between Privacy and Utility in Data Provenance

Daniel Deutch, Ariel Frankenthal, Amir Gilad +1

Organizations that collect and analyze data may wish or be mandated by regulation to justify and explain their analysis results. At the same time, the logic that they have followed…

cs.DB2020

Patterns Count-Based Labels for Datasets

Yuval Moskovitch, H. V. Jagadish

Counts of attribute-value combinations are central to the profiling of a dataset, particularly in determining fitness for use and in eliminating bias and unfairness. While counts o…