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Found 2 papers, 2 papers with code
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas.
Differentially Private Topological Data Analysis
We analyze the sensitivity of persistence diagrams in terms of the bottleneck distance, and we show that the commonly used \v{C}ech complex has sensitivity that does not decrease as the sample size $n$ increases.
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