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OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport

Summary: OTClean repairs datasets to enforce conditional independence (CI) constraints for fair/trustworthy ML, using optimal transport as the distortion metric. Formulates CI repair as a QCLP, then scales it via Sinkhorn-style regularized optimization for high-dimensional data while preserving utility. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6924
Venue
SIGMOD
Year
2024
Pagerank
4.7269357e-05
Overall Rank
7,449 | 48.18%
DOI
10.1145/3654963

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Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
9,644 Fair and Actionable Causal Prescription Ruleset 2025 SIGMOD 4.3109001e-05
9,984 Towards Scalable Visual Data Wrangling via Direct Manipulation 2026 CIDR 4.1945683e-05
10,101 Privacy-preserving and Verifiable Causal Prescriptive Analytics 2026 SIGMOD 4.1945683e-05
10,213 Stress-Testing Causal Claims via Cardinality Repairs 2026 SIGMOD 4.1945683e-05
10,395 User-Centric Property Graph Repairs 2025 SIGMOD 4.1945683e-05
10,581 Causal DAG Summarization 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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