iFlipper: Label Flipping for Individual Fairness
Summary: iFlipper uses label flipping as pre-processing to enforce individual fairness by minimizing flips within a bound on violations among similar instances. NP-hard; an approximate LP with guarantees yields near-optimal flips; optimizations boost fairness/accuracy and it outperforms pre-processing baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hantian Zhang
- 2. Ki Hyun Tae
- 3. Jaeyoung Park
- 4. Xu Chu
- 5. Steven Euijong Whang
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,373 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB | 4.3460825e-05 |
| 9,644 | Fair and Actionable Causal Prescription Ruleset | 2025 | SIGMOD | 4.3067693e-05 |
| 10,223 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 4.1905499e-05 |
| 10,488 | Data Enhancement for Binary Classification of Relational Data | 2025 | SIGMOD | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 252 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030532082 |
| 1,037 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD | 0.00014514825 |
| 4,088 | Snorkel: Fast Training Set Generation for Information Extraction | 2017 | SIGMOD | 6.457048e-05 |
| 4,866 | OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning | 2021 | SIGMOD | 5.8620848e-05 |
| 5,407 | Operationalizing Individual Fairness with Pairwise Fair Representations | 2020 | VLDB | 5.523874e-05 |
Previous
Page 1 / 1
Next