Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data | Paper Citation Record

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This paper cites Tumult Labs.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Tumult Labs

Reference 1

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This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

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Reference 8

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This paper cites 2023.Fairness and Machine Learning: Limitations and Opportunities.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data 2023.Fairness and Machine Learning: Limitations and Opportunities

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Reference 10

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Reference 11

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Reference 12

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This paper cites Evaluating the Fairness Impact of Differentially Private Synthetic Data.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Evaluating the Fairness Impact of Differentially Private Synthetic Data

Reference 14

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data 1999.Practical nonparametric statistics

Reference 19

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Unresolved cited work

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Truly Anonymous

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Synthetic Data -- what, why and how?

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

Reference 40

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Arcolezi, and Catuscia Palamidessi

Reference 42

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Winning the NIST Contest: A scalable and general approach to differentially private synthetic data

Reference 43

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data 2023.Probabilistic machine learning: Advanced topics

Reference 46

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Benchmarking Differentially Private Synthetic Data Generation Algorithms

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Census Bureau

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Male”sexto 1 and others to 0; (ii) maps “White

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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Unresolved cited work

Reference 2019

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Observation 3aa4d244-5895-4671-9eb5-0260a8ddfadc · outbound

This paper cites InProceedings of the 2020 workshop on privacy-preserving machine learning in practice.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data InProceedings of the 2020 workshop on privacy-preserving machine learning in practice

Reference 2020

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Observation ff72d91f-ef1f-4fba-b364-7c71c7d262a1 · outbound

This paper cites Proceedings on Privacy Enhancing Technologies2 (2023), 312–328.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Proceedings on Privacy Enhancing Technologies2 (2023), 312–328

Reference 2023

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Observation a0cf68a5-d933-4712-bfec-b6609399526d · outbound

This paper cites InECAI 2025.

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data InECAI 2025

Reference 2025

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