14 citations · 56 across the 16 of their papers we have counts for
19 papers
Fast and Private Max-Sum Diversification
Ron Zadicario, Tova Milo
Result diversification is crucial for generating informative, non-redundant data summaries and query outputs. Although its various formulations have been extensively studied across…
Differentially Private Submodular Maximization with a Knapsack Constraint
Ron Zadicario, Tova Milo
Submodular maximization subject to a knapsack constraint (SMK) is a fundamental problem in discrete optimization, with wide-ranging applications in machine learning and related fie…
Differentially Private Explanations for Clusters
Amir Gilad, Tova Milo, Kathy Razmadze +1
The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure…
LINX: A Language Driven Generative System for Goal-Oriented Automated Data Exploration
Tavor Lipman, Tova Milo, Amit Somech +2
Data exploration is a challenging process in which users examine a dataset by iteratively employing a series of queries. While in some cases the user explores a new dataset to beco…
Learning Approximation Sets for Exploratory Queries
Susan B. Davidson, Tova Milo, Kathy Razmadze +1
In data exploration, executing complex non-aggregate queries over large databases can be time-consuming. Our paper introduces a novel approach to address this challenge, focusing o…
FEDEX: An Explainability Framework for Data Exploration Steps
Daniel Deutch, Amir Gilad, Tova Milo +2
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this pa…