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20102022
most citedComputing Local Sensitivities of Counting Queries with Joins

37 citations · 149 across the 26 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.DB2020

Aggregated Deletion Propagation for Counting Conjunctive Query Answers

Xiao Hu, Shouzhuo Sun, Shweta Patwa +2

We investigate the computational complexity of minimizing the source side-effect in order to remove a given number of tuples from the output of a conjunctive query. This is a varia…

cs.DB2020★ 20 cited

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…

cs.DB2020★ 37 cited

Computing Local Sensitivities of Counting Queries with Joins

Yuchao Tao, Xi He, Ashwin Machanavajjhala +1

Local sensitivity of a query Q given a database instance D, i.e. how much the output Q(D) changes when a tuple is added to D or deleted from D, has many applications including quer…

cs.DB2020★ 6 cited

Causal Relational Learning

Babak Salimi, Harsh Parikh, Moe Kayali +3

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in ca…

stat.ME2020★ 4 cited

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

M. Usaid Awan, Marco Morucci, Vittorio Orlandi +3

We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can pot…

stat.ME2020★ 2 cited

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

Marco Morucci, Vittorio Orlandi, Sudeepa Roy +2

We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough…