Learning Fair Representations via an Adversarial Framework . Title: Learning Fair Representations via an Adversarial Framework.. To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and.
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To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the.
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ent adversarial objectives. Through worst-case theoretical guarantees and experimental valida-tion, we show that the choice of this objective is crucial to fair prediction. Furthermore, we.
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thereby also learning the trade-off between expressive-ness and fairness. We further show that our proposed framework is strongly convex in distribution space. Our work is the first to.
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Learning Fair Representations via an Adversarial Framework. This is the code for implementation/reproduction of experiments described in paper https://arxiv.org/abs.
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To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the.
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A minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the distributions across different protected.
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Learning Fair Representations via an Adversarial Framework. Click To Get Model/Code. Fairness has become a central issue for our research community as classification algorithms are.
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Learning Fair Representations. R. Zemel, Ledell Yu Wu, +2 authors. C. Dwork. Published in ICML 16 June 2013. Computer Science. We propose a learning algorithm for fair.
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The FS procedure uses a backward feature selection strategy, where a regression model based on Ordinary Least Squares (OLS) is used to predict A and variables with p-values.
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Learning Fair Representations via an Adversarial Framework. Fairness has become a central issue for our research community as classification algorithms are adopted in.
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Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this.
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Abstract: We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be.
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4 Adversarially Fair Representations 4.1 A Generalized Model. We assume a generalized model (Figure 1 ), which seeks to learn a data representation Z capable... 4.2.
