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Amir Gilad
Person information
- affiliation: Duke University, Durham, NC, USA
- affiliation: Tel Aviv University, Tel Aviv, Israel
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2020 – today
- 2024
- [j19]Brit Youngmann
, Michael J. Cafarella
Summarized Causal Explanations For Aggregate Views. Proc. ACM Manag. Data 2(1): 71:1-71:27 (2024) - [j18]Yihao Hu
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries. Proc. ACM Manag. Data 2(3): 164 (2024) - [j17]Itay Elyashiv, Amir Gilad, Edna Isakov, Tal Tikochinsky, Amit Somech:
PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames. Proc. VLDB Endow. 17(12): 4473-4476 (2024) - [c21]Jun Yang
What Teaching Databases Taught Us about Researching Databases: Extended Talk Abstract. DataEd@SIGMOD 2024 - [c20]Sudeepa Roy, Amir Gilad, Yihao Hu, Hanze Meng, Zhengjie Miao, Kristin Stephens-Martinez, Jun Yang:
How Database Theory Helps Teach Relational Queries in Database Education (Invited Talk). ICDT 2024: 2:1-2:9 - [c19]Abolfazl Asudeh
First Workshop on Governance, Understanding and Integration of Data for Effective and Responsible AI (GUIDE-AI). SIGMOD Conference Companion 2024: 661-662 - [i17]Yihao Hu, Amir Gilad, Kristin Stephens-Martinez, Sudeepa Roy, Jun Yang:
Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries. CoRR abs/2404.04352 (2024) - [i16]Shunit Agmon, Amir Gilad, Brit Youngmann, Shahar Zoarets, Benny Kimelfeld:
Finding Convincing Views to Endorse a Claim. CoRR abs/2408.14974 (2024) - 2023
- [j16]David Pujol, Amir Gilad, Ashwin Machanavajjhala:
PreFair: Privately Generating Justifiably Fair Synthetic Data. Proc. VLDB Endow. 16(6): 1573-1586 (2023) - [j15]Tingyu Wang, Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
Explaining Differentially Private Query Results With DPXPlain. Proc. VLDB Endow. 16(12): 3962-3965 (2023) - [j14]Shweta Patwa, Danyu Sun, Amir Gilad
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. Proc. VLDB Endow. 17(1): 65-78 (2023) - [c18]Fangzhu Shen, Kayvon Heravi, Oscar Gomez, Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi:
Causal What-If and How-To Analysis Using HypeR. ICDE 2023: 3663-3666 - [c17]Amir Gilad, Aviram Imber, Benny Kimelfeld:
The Consistency of Probabilistic Databases with Independent Cells. ICDT 2023: 22:1-22:19 - [c16]Hanze Meng
Characterizing and Verifying Queries Via CINSGEN. SIGMOD Conference Companion 2023: 143-146 - [i15]Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. CoRR abs/2309.08574 (2023) - 2022
- [j13]Daniel Deutch, Amir Gilad, Tova Milo, Amit Mualem, Amit Somech
FEDEX: An Explainability Framework for Data Exploration Steps. Proc. VLDB Endow. 15(13): 3854-3868 (2022) - [j12]Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DPXPlain: Privately Explaining Aggregate Query Answers. Proc. VLDB Endow. 16(1): 113-126 (2022) - [c15]Amir Gilad, Zhengjie Miao, Sudeepa Roy, Jun Yang:
Understanding Queries by Conditional Instances. SIGMOD Conference 2022: 355-368 - [c14]Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi:
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach. SIGMOD Conference 2022: 1598-1611 - [i14]Amir Gilad, Zhengjie Miao, Sudeepa Roy, Jun Yang:
Understanding Queries by Conditional Instances. CoRR abs/2202.11160 (2022) - [i13]Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi:
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach. CoRR abs/2203.14692 (2022) - [i12]Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DPXPlain: Privately Explaining Aggregate Query Answers. CoRR abs/2209.01286 (2022) - [i11]Daniel Deutch, Amir Gilad, Tova Milo, Amit Mualem, Amit Somech:
FEDEX: An Explainability Framework for Data Exploration Steps. CoRR abs/2209.06260 (2022) - [i10]David Pujol, Amir Gilad, Ashwin Machanavajjhala:
PreFair: Privately Generating Justifiably Fair Synthetic Data. CoRR abs/2212.10310 (2022) - [i9]Amir Gilad, Aviram Imber, Benny Kimelfeld:
The Consistency of Probabilistic Databases with Independent Cells. CoRR abs/2212.12104 (2022) - 2021
- [c13]Daniel Deutch, Nave Frost, Amir Gilad, Oren Sheffer:
Explanations for Data Repair Through Shapley Values. CIKM 2021: 362-371 - [c12]Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch:
PITA: Privacy Through Provenance Abstraction. ICDE 2021: 2713-2716 - [c11]Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch:
On Optimizing the Trade-off between Privacy and Utility in Data Provenance. SIGMOD Conference 2021: 379-391 - [c10]Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala:
Synthesizing Linked Data Under Cardinality and Integrity Constraints. SIGMOD Conference 2021: 619-631 - [i8]Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch:
On Optimizing the Trade-off between Privacy and Utility in Data Provenance. CoRR abs/2103.00288 (2021) - [i7]Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala:
Synthesizing Linked Data Under Cardinality and Integrity Constraints. CoRR abs/2103.14435 (2021) - [i6]Amir Gilad, Harsh Parikh, Babak Salimi, Sudeepa Roy:
Detecting Treatment Effect Modifiers in Social Networks. CoRR abs/2105.10591 (2021) - 2020
- [b1]Amir Gilad:
Data Provenance for Non-Experts. Tel Aviv University, Israel, 2020 - [j11]Daniel Deutch, Amir Gilad, Tova Milo, Amit Somech
ExplainED: Explanations for EDA Notebooks. Proc. VLDB Endow. 13(12): 2917-2920 (2020) - [j10]Amir Gilad, Yihao Hu, Daniel Deutch, Sudeepa Roy:
MuSe: Multiple Deletion Semantics for Data Repair. Proc. VLDB Endow. 13(12): 2921-2924 (2020) - [j9]Daniel Deutch, Nave Frost, Amir Gilad
Explaining Natural Language query results. VLDB J. 29(1): 485-508 (2020) - [c9]Amir Gilad, Yuval Moskovitch:
Towards Inferring Queries from Simple and Partial Provenance Examples. CIKM 2020: 3273-3276 - [c8]Daniel Deutch, Nave Frost, Amir Gilad, Tomer Haimovich:
Explaining Missing Query Results in Natural Language. EDBT 2020: 427-430 - [c7]Amir Gilad, Daniel Deutch, Sudeepa Roy:
On Multiple Semantics for Declarative Database Repairs. SIGMOD Conference 2020: 817-831 - [c6]Daniel Deutch, Nave Frost, Amir Gilad, Oren Sheffer:
T-REx: Table Repair Explanations. SIGMOD Conference 2020: 2765-2768 - [i5]Amir Gilad, Daniel Deutch, Sudeepa Roy:
On Multiple Semantics for Declarative Database Repairs. CoRR abs/2004.05065 (2020) - [i4]Daniel Deutch, Nave Frost, Amir Gilad, Oren Sheffer:
T-REx: Table Repair Explanations. CoRR abs/2007.04450 (2020) - [i3]Daniel Deutch, Nave Frost, Amir Gilad:
Explaining Natural Language Query Results. CoRR abs/2007.04454 (2020) - [i2]Amir Gilad, Yuval Moskovitch:
Towards Inferring Queries from Simple and Partial Provenance Examples. CoRR abs/2008.08989 (2020)
2010 – 2019
- 2019
- [c5]Daniel Deutch, Amir Gilad:
Reverse-Engineering Conjunctive Queries from Provenance Examples. EDBT 2019: 277-288 - 2018
- [j8]Daniel Deutch, Nave Frost, Amir Gilad:
Provenance for Non-Experts. IEEE Data Eng. Bull. 41(1): 3-14 (2018) - [j7]Daniel Deutch, Nave Frost, Amir Gilad, Tomer Haimovich:
NLProveNAns: Natural Language Provenance for Non-Answers. Proc. VLDB Endow. 11(12): 1986-1989 (2018) - [j6]Efrat Abramovitz, Daniel Deutch, Amir Gilad:
QuestPro: Queries in SPARQL Through Provenance. Proc. VLDB Endow. 11(12): 1994-1997 (2018) - [j5]Daniel Deutch, Nave Frost, Amir Gilad:
Natural Language Explanations for Query Results. SIGMOD Rec. 47(1): 42-49 (2018) - [j4]Daniel Deutch, Amir Gilad
Efficient provenance tracking for datalog using top-k queries. VLDB J. 27(2): 245-269 (2018) - [c4]Efrat Abramovitz, Daniel Deutch, Amir Gilad:
Interactive Inference of SPARQL Queries Using Provenance. ICDE 2018: 581-592 - 2017
- [j3]Daniel Deutch, Nave Frost, Amir Gilad:
Provenance for Natural Language Queries. Proc. VLDB Endow. 10(5): 577-588 (2017) - 2016
- [j2]Daniel Deutch, Nave Frost, Amir Gilad:
NLProv: Natural Language Provenance. Proc. VLDB Endow. 9(13): 1537-1540 (2016) - [c3]Daniel Deutch, Amir Gilad:
QPlain: Query by explanation. ICDE 2016: 1358-1361 - [i1]Daniel Deutch, Amir Gilad:
Learning Queries from Examples and Their Explanations. CoRR abs/1602.03819 (2016) - 2015
- [j1]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
Selective Provenance for Datalog Programs Using Top-K Queries. Proc. VLDB Endow. 8(12): 1394-1405 (2015) - [c2]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
Towards web-scale how-provenance. ICDE Workshops 2015: 68-70 - [c1]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
selP: Selective tracking and presentation of data provenance. ICDE 2015: 1484-1487
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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