2 citations · 4 across the 12 of their papers we have counts for
12 papers
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.…
A General LLM Framework for Query Refinement
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 its output satisfies a specified set of constrain…
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…
Query Refinement for Diverse Top- Selection
Felix S. Campbell, Alon Silberstein, Julia Stoyanovich +1
Database queries are often used to select and rank items as decision support for many applications. As automated decision-making tools become more prevalent, there is a growing rec…
Detection of Groups with Biased Representation in Ranking
Jinyang Li, Yuval Moskovitch, H. V. Jagadish
Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measur…
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…
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…
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…
Towards Inferring Queries from Simple and Partial Provenance Examples
Amir Gilad, Yuval Moskovitch
The field of query-by-example aims at inferring queries from output examples given by non-expert users, by finding the underlying logic that binds the examples. However, for a very…
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries
Pierre Bourhis, Daniel Deutch, Yuval Moskovitch
The algebraic approach for provenance tracking, originating in the semiring model of Green et. al, has proven useful as an abstract way of handling metadata. Commutative Semirings…
Hypothetical Reasoning via Provenance Abstraction
Daniel Deutch, Yuval Moskovitch, Noam Rinetzky
Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Previo…
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning
Daniel Deutch, Yuval Moskovitch, Noam Rinetzky
Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Recent…