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Found 8 papers, 4 papers with code
f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
While numerous constraints and regularization terms have been proposed in the literature to promote fairness in machine learning tasks, most of these methods are not amenable to stochastic optimization due to the complex and nonlinear structure of constraints and regularizers.
Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework
While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions.
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
This work concerns the development of deep networks that are certifiably robust to adversarial attacks.
RIFLE: Imputation and Robust Inference from Low Order Marginals
We develop a statistical inference framework for regression and classification in the presence of missing data without imputation.
A Stochastic Optimization Framework for Fair Risk Minimization
We consider the problem of fair classification with discrete sensitive attributes and potentially large models and data sets, requiring stochastic solvers.
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?
We study the optimization problem for decomposing $d$ dimensional fourth-order Tensors with $k$ non-orthogonal components.
Rényi Fair Inference
In this paper, we use R\'enyi correlation as a measure of fairness of machine learning models and develop a general training framework to impose fairness.
