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Zhou Zhai

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AAAI Conference 2023 Conference Paper

Faster Fair Machine via Transferring Fairness Constraints to Virtual Samples

Zhou Zhai
Lei Luo
Heng Huang
Bin Gu

Fair classification is an emerging and important research topic in machine learning community. Existing methods usually formulate the fairness metrics as additional inequality constraints, and then embed them into the original objective. This makes fair classification problems unable to be effectively tackled by some solvers specific to unconstrained optimization. Although many new tailored algorithms have been designed to attempt to overcome this limitation, they often increase additional computation burden and cannot cope with all types of fairness metrics. To address these challenging issues, in this paper, we propose a novel method for fair classification. Specifically, we theoretically demonstrate that all types of fairness with linear and non-linear covariance functions can be transferred to two virtual samples, which makes the existing state-of-the-art classification solvers be applicable to these cases. Meanwhile, we generalize the proposed method to multiple fairness constraints. We take SVM as an example to show the effectiveness of our new idea. Empirically, we test the proposed method on real-world datasets and all results confirm its excellent performance.

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AAAI Conference 2020 Conference Paper

Safe Sample Screening for Robust Support Vector Machine

Zhou Zhai
Bin Gu
Xiang Li
Heng Huang

Robust support vector machine (RSVM) has been shown to perform remarkably well to improve the generalization performance of support vector machine under the noisy environment. Unfortunately, in order to handle the non-convexity induced by ramp loss in RSVM, existing RSVM solvers often adopt the DC programming framework which is computationally inefﬁcient for running multiple outer loops. This hinders the application of RSVM to large-scale problems. Safe sample screening that allows for the exclusion of training samples prior to or early in the training process is an effective method to greatly reduce computational time. However, existing safe sample screening algorithms are limited to convex optimization problems while RSVM is a non-convex problem. To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP). Speciﬁcally, we provide screening rule for the inner solver of CCCP and another rule for propagating screened samples between two successive solvers of CCCP. To the best of our knowledge, this is the ﬁrst work of safe sample screening to a non-convex optimization problem. More importantly, we provide the security guarantee to our sample screening rules to RSVM. Experimental results on a variety of benchmark datasets verify that our safe sample screening rules can signiﬁcantly reduce the computational time.

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