20 citations · 51 across the 18 of their papers we have counts for
20 papers
Refining Labeling Functions with Limited Labeled Data
Chenjie Li, Amir Gilad, Boris Glavic +2
Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints.…
Computing Inconsistency Measures Under Differential Privacy
Shubhankar Mohapatra, Amir Gilad, Xi He +1
Assessing data quality is crucial to knowing whether and how to use the data for different purposes. Specifically, given a collection of integrity constraints, various ways have be…
The Cost of Representation by Subset Repairs
Yuxi Liu, Fangzhu Shen, Kushagra Ghosh +3
Datasets may include errors, and specifically violations of integrity constraints, for various reasons. Standard techniques for ``minimal-cost'' database repairing resolve these vi…
Summarized Causal Explanations For Aggregate Views (Full version)
Brit Youngmann, Michael Cafarella, Amir Gilad +1
SQL queries with group-by and average are frequently used and plotted as bar charts in several data analysis applications. Understanding the reasons behind the results in such an a…
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries
Yihao Hu, Amir Gilad, Kristin Stephens-Martinez +2
We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the…
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms
Shweta Patwa, Danyu Sun, Amir Gilad +2
Synthetic data generation methods, and in particular, private synthetic data generation methods, are gaining popularity as a means to make copies of sensitive databases that can be…
The Consistency of Probabilistic Databases with Independent Cells
Amir Gilad, Aviram Imber, Benny Kimelfeld
A probabilistic database with attribute-level uncertainty consists of relations where cells of some attributes may hold probability distributions rather than deterministic content.…
PreFair: Privately Generating Justifiably Fair Synthetic Data
David Pujol, Amir Gilad, Ashwin Machanavajjhala
When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the propertie…
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…
DPXPlain: Privately Explaining Aggregate Query Answers
Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala +1
Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in t…
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach
Sainyam Galhotra, Amir Gilad, Sudeepa Roy +1
What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical sce…
Understanding Queries by Conditional Instances
Amir Gilad, Zhengjie Miao, Sudeepa Roy +1
A powerful way to understand a complex query is by observing how it operates on data instances. However, specific database instances are not ideal for such observations: they often…
Heterogeneous Treatment Effects in Social Networks
Amir Gilad, Harsh Parikh, Sudeepa Roy +1
We study treatment effect modifiers for causal analysis in a social network, where neighbors' characteristics or network structure may affect the outcome of a unit, and the goal is…
Synthesizing Linked Data Under Cardinality and Integrity Constraints
Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala
The generation of synthetic data is useful in multiple aspects, from testing applications to benchmarking to privacy preservation. Generating the links between relations, subject t…
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…
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…
Explaining Natural Language Query Results
Daniel Deutch, Nave Frost, Amir Gilad
Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form…
T-REx: Table Repair Explanations
Daniel Deutch, Nave Frost, Amir Gilad +1
Data repair is a common and crucial step in many frameworks today, as applications may use data from different sources and of different levels of credibility. Thus, this step has b…
On Multiple Semantics for Declarative Database Repairs
Amir Gilad, Daniel Deutch, Sudeepa Roy
We study the problem of database repairs through a rule-based framework that we refer to as Delta Rules. Delta Rules are highly expressive and allow specifying complex, cross-relat…
Query By Provenance
Daniel Deutch, Amir Gilad
To assist non-specialists in formulating database queries, multiple frameworks that automatically infer queries from a set of examples have been proposed. While highly useful, a sh…