text: 213,11,1388549201819312129,1661813766,Mehdi Kamani,"New paper! We introduce a first-order algorithm: • Converges to a point on the #ParetoFrontier with the desired level of trade-offs in #MultiobjectiveOptimization • Traces other points on the Pareto frontier • SOTA results on #Fairness aware learning <LINK> <LINK> Using the proposed Preference-based Pareto Descent Optimization, unlike other approaches we can trace other points on the Pareto frontier using only first-order information while converging to the desired point on that set. #MultiobjectiveOptimization #Pareto <LINK> The application of this approach to the #fairness problem shows SOTA performance with finding many points on the Pareto frontier for better and enhanced decision making in terms of fairness. Solutions found by our algorithm mostly dominate other SOTA's solutions. Please RT! <LINK>",https://arxiv.org/abs/2104.01634,"As algorithmic decision-making systems are becoming more pervasive, it is crucial to ensure such systems do not become mechanisms of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. Moreover, due to the inherent trade-off between fairness measures and accuracy, it is desirable to learn fairness-enhanced models without significantly compromising the accuracy. In this paper, we propose Pareto efficient Fairness (PEF) as a suitable fairness notion for supervised learning, that can ensure the optimal trade-off between overall loss and other fairness criteria. The proposed PEF notion is definition-agnostic, meaning that any well-defined notion of fairness can be reduced to the PEF notion. To efficiently find a PEF classifier, we cast the fairness-enhanced classification as a bilevel optimization problem and propose a gradient-based method that can guarantee the solution belongs to the Pareto frontier with provable guarantees for convex and non-convex objectives. We also generalize the proposed algorithmic solution to extract and trace arbitrary solutions from the Pareto frontier for a given preference over accuracy and fairness measures. This approach is generic and can be generalized to any multicriteria optimization problem to trace points on the Pareto frontier curve, which is interesting by its own right. We empirically demonstrate the effectiveness of the PEF solution and the extracted Pareto frontier on real-world datasets compared to state-of-the-art methods. ","Pareto Efficient Fairness in Supervised Learning: From Extraction to