Wasserstein Robust Support Vector Machines with Fairness Constraints
- URL: http://arxiv.org/abs/2103.06828v1
- Date: Thu, 11 Mar 2021 17:53:54 GMT
- Title: Wasserstein Robust Support Vector Machines with Fairness Constraints
- Authors: Yijie Wang, Viet Anh Nguyen, Grani A. Hanasusanto
- Abstract summary: We use a type-$infty$ Wasserstein ambiguity set centered at the empirical distribution to model distributional uncertainty.
We numerically demonstrate that our proposed approach improves fairness with negligible loss of predictive accuracy.
- Score: 15.004754864933705
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a distributionally robust support vector machine with a fairness
constraint that encourages the classifier to be fair in view of the equality of
opportunity criterion. We use a type-$\infty$ Wasserstein ambiguity set
centered at the empirical distribution to model distributional uncertainty and
derive an exact reformulation for worst-case unfairness measure. We establish
that the model is equivalent to a mixed-binary optimization problem, which can
be solved by standard off-the-shelf solvers. We further prove that the
expectation of the hinge loss objective function constitutes an upper bound on
the misclassification probability. Finally, we numerically demonstrate that our
proposed approach improves fairness with negligible loss of predictive
accuracy.
Related papers
- Rejection via Learning Density Ratios [50.91522897152437]
Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions.
We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.
Our framework is tested empirically over clean and noisy datasets.
arXiv Detail & Related papers (2024-05-29T01:32:17Z) - An Inexact Halpern Iteration with Application to Distributionally Robust
Optimization [9.529117276663431]
We investigate the inexact variants of the scheme in both deterministic and deterministic convergence settings.
We show that by choosing the inexactness appropriately, the inexact schemes admit an $O(k-1) convergence rate in terms of the (expected) residue norm.
arXiv Detail & Related papers (2024-02-08T20:12:47Z) - Regularized Vector Quantization for Tokenized Image Synthesis [126.96880843754066]
Quantizing images into discrete representations has been a fundamental problem in unified generative modeling.
deterministic quantization suffers from severe codebook collapse and misalignment with inference stage while quantization suffers from low codebook utilization and reconstruction objective.
This paper presents a regularized vector quantization framework that allows to mitigate perturbed above issues effectively by applying regularization from two perspectives.
arXiv Detail & Related papers (2023-03-11T15:20:54Z) - Fairness in Matching under Uncertainty [78.39459690570531]
algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings.
We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits.
We design a linear programming framework to find fair utility-maximizing distributions over allocations.
arXiv Detail & Related papers (2023-02-08T00:30:32Z) - A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization [11.034091190797671]
We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probability distribution.
We demonstrate that the interchangeability principle holds if and only if certain measurable projection and weak measurable selection conditions are satisfied.
arXiv Detail & Related papers (2022-04-30T22:49:01Z) - Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
The trade-off between robustness and accuracy has been widely studied in the adversarial literature.
We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance.
By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
arXiv Detail & Related papers (2022-02-21T10:36:09Z) - Beyond Individual and Group Fairness [90.4666341812857]
We present a new data-driven model of fairness that is guided by the unfairness complaints received by the system.
Our model supports multiple fairness criteria and takes into account their potential incompatibilities.
arXiv Detail & Related papers (2020-08-21T14:14:44Z) - A Distributionally Robust Approach to Fair Classification [17.759493152879013]
We propose a robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity.
This model is equivalent to a tractable convex optimization problem if a Wasserstein ball centered at the empirical distribution on the training data is used to model distributional uncertainty.
We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets.
arXiv Detail & Related papers (2020-07-18T22:34:48Z) - Fair Regression with Wasserstein Barycenters [39.818025466204055]
We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint.
It demands the distribution of the predicted output to be independent of the sensitive attribute.
We establish a connection between fair regression and optimal transport theory, based on which we derive a close form expression for the optimal fair predictor.
arXiv Detail & Related papers (2020-06-12T16:10:41Z) - Distributionally Robust Bayesian Quadrature Optimization [60.383252534861136]
We study BQO under distributional uncertainty in which the underlying probability distribution is unknown except for a limited set of its i.i.d. samples.
A standard BQO approach maximizes the Monte Carlo estimate of the true expected objective given the fixed sample set.
We propose a novel posterior sampling based algorithm, namely distributionally robust BQO (DRBQO) for this purpose.
arXiv Detail & Related papers (2020-01-19T12:00:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.