Publications

Amir Gilad, Tova Milo, Kathy Razmadze, Ron Zadicario (2025). Differentially Private Explanations for Clusters. In SIGMOD.

Shubhankar Mohapatra, Amir Gilad, Xi He, Benny Kimelfeld (2025). Computing Inconsistency Measures Under Differential Privacy. In SIGMOD.

Yuchao Tao, Amir Gilad, Sudeepa Roy, Ashwin Machanavajjhala (2025). Differentially private explanations for aggregate query answers. In VLDB J. 34(2), Best of VLDB Special Issue.

Yuxi Liu, Fangzhu Shen, Kushagra Ghosh, Amir Gilad, Benny Kimelfeld, Sudeepa Roy (2024). The Cost of Representation by Subset Repairs. In PVLDB 18(2), 2025.

Shunit Agmon, Amir Gilad, Brit Youngmann, Shahar Zoarets, Benny Kimelfeld (2024). Finding Convincing Views to Endorse a Claim. In PVLDB 18(2), 2025.

Itay Elyashiv, Amir Gilad, Edna Isakov, Tal Tikochinsky, Amit Somech (2024). PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames. In PVLDB 17(12), 2024.

Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy (2024). DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. In PVLDB 17(1), 2024.

Brit Youngmann, Michael Cafarella, Amir Gilad, Sudeepa Roy (2024). Summarized Causal Explanations For Aggregate Views. In SIGMOD.

Yihao Hu, Amir Gilad, Sudeepa Roy, Jun Ynag (2024). Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries. In SIGMOD.

Hanze Meng, Zhengjie Miao, Amir Gilad, Sudeepa Roy, Jun Yang (2023). Characterizing and Verifying Queries Via CINSGEN. In SIGMOD.

Amir Gilad, Aviram Imber, Benny Kimelfeld (2023). The Consistency of Probabilistic Databases with Independent Cells. In ICDT, 2023.

David Pujol, Amir Gilad, Ashwin Machanavajjhala (2023). PreFair: Privately Generating Justifiably Fair Synthetic Data. In PVLDB 16(5), 2023.

Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy (2023). DPXPlain: Privately Explaining Aggregate Query Answers. In PVLDB 16(1), 2023.

Daniel Deutch, Amir Gilad, Tova Milo, Amit Mualem, Amit Somech (2023). FEDEX: An Explainability Framework for Data Exploration Steps. In PVLDB 15(13), 2023.

Fangzhu Shen, Kayvon Heravi, Oscar Gomez, Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi (2023). Causal What-If and How-To Analysis Using HYPER. In ICDE, 2023.

Amir Gilad, Zhengjie Miao, Sudeepa Roy, Jun Yang (2022). Understanding Queries by Conditional Instances. In SIGMOD.

Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi (2022). HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach. In SIGMOD.

Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala (2021). Synthesizing Linked Data Under Cardinality and Integrity Constraints. In SIGMOD.

Daniel Deutch, Nave Frost, Amir Gilad, Oren Sheffer (2021). Explanations for Data Repair Through Shapley Values. In CIKM.

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch (2021). On Optimizing the Trade-off between Privacy and Utility in Data Provenance. In SIGMOD.

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch (2021). PITA: Privacy Through Provenance Abstraction. In ICDE.

Amir Gilad, Yuval Moskovitch (2020). Towards Inferring Queries from Simple and Partial Provenance Examples. In CIKM.

Amir Gilad, Yihao Hu, Daniel Deutch, Sudeepa Roy (2020). MuSe: Multiple Deletion Semantics for Data Repair. In PVLDB 13(12).

Daniel Deutch, Amir Gilad, Tova Milo, Amit Somech (2020). ExplainED: Explanations for EDA Notebooks. In PVLDB 13(12).

Daniel Deutch, Nave Frost, Amir Gilad, Oren Sheffer (2020). T-REx: Table Repair Explanations. In SIGMOD.

Amir Gilad, Daniel Deutch, Sudeepa Roy (2020). On Multiple Semantics for Declarative Database Repairs. In SIGMOD.

Daniel Deutch, Nave Frost, Amir Gilad, Tomer Haimovich (2020). Explaining Missing Query Results in Natural Language. In EDBT.

Daniel Deutch, Nave Frost, Amir Gilad (2020). Explaining Natural Language Query Results. In VLDB J. 29(1).

Daniel Deutch, Amir Gilad (2019). Reverse-Engineering Conjunctive Queries from Provenance Examples. In EDBT.

Efrat Abramovitz, Daniel Deutch, Amir Gilad (2018). QuestPro: Queries in SPARQL Through Provenance. In PVLDB 11(12).

Daniel Deutch, Nave Frost, Amir Gilad, Tomer Haimovich (2018). NLProveNAns: Natural Language Provenance for Non-Answers. In PVLDB 11(12).

Daniel Deutch, Nave Frost, Amir Gilad (2018). Natural Language Explanations for Query Results. In SIGMOD Record 47(1), SIGMOD Research Highlight.

Daniel Deutch, Amir Gilad, Yuval Moskovitch (2018). Efficient Provenance Tracking For Datalog Using Top-K Queries. In VLDB J. 27(2).

Efrat Abramovitz, Daniel Deutch, Amir Gilad (2018). Interactive Inference of SPARQL Queries Using Provenance. In ICDE.

Daniel Deutch, Nave Frost, Amir Gilad (2018). Provenance for Non-Experts. In IEEE Data Engineering Bulletin 41(1).

Daniel Deutch, Nave Frost, Amir Gilad (2017). Provenance for Natural Language Queries. In PVLDB 10(5), Best Paper Award.

Daniel Deutch, Nave Frost, Amir Gilad (2016). NLProv: Natural Language Provenance. In PVLDB 9(13).

Daniel Deutch, Amir Gilad (2016). QPlain: Query By Explanation. In ICDE.

Daniel Deutch, Amir Gilad, Yuval Moskovitch (2015). Selective Provenance for Datalog Programs Using Top-K Queries. In PVLDB 8(12).

Daniel Deutch, Amir Gilad, Yuval Moskovitch (2015). Towards web-scale how-provenance. In DESWeb.

Daniel Deutch, Amir Gilad, Yuval Moskovitch (2015). selP: Selective tracking and presentation of data provenance. In ICDE.