The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigation in binary classification tasks, the issue remains under-explored in multi-class classification settings. To address this limitation, in this paper, we first formulate the problem of fair learning in multi-class classification as a multi-objective problem between effectiveness (i.e., prediction correctness) and multiple linear fairness constraints. Next, we propose a Generalised Exponentiated Gradient (GEG) algorithm to solve this task. GEG is an in-processing algorithm that enhances fairness in binary and multi-class classification settings under multiple fairness definitions. We conduct an extensive empirical evaluation of GEG against six baselines across seven multi-class and three binary datasets, using four widely adopted effectiveness metrics and three fairness definitions. GEG overcomes existing baselines, with fairness improvements up to 92% and a decrease in accuracy up to 14%.

New photography book takes readers on a journey down the world's longest river. Magnificent frigate birds (Fregata magnificens) make long foraging trips far over the Atlantic Ocean. Breakthroughs, discoveries, and DIY tips sent six days a week. The vital Amazon River is a lifeline for flora and fauna alike. The mighty river is celebrated in a new book, .

The sophisticated network crossed mountains in Peru and pre-dates the Inca Empire. Breakthroughs, discoveries, and DIY tips sent six days a week. Ancient parrots really got around. A new analysis of their DNA found that humans transported living Amazonian macaw parrots across the Andes mountains to coastal Peru hundreds of years before the Inca Empire. The findings are detailed in a study published today in the journal and reveal a highly sophisticated and long-distance bird trading network across deserts, highlands, and rainforests.

V olume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data.

When turned on, "GitHub Copilot checks code completion suggestions with their surrounding

In the tabular setting, we present a novel mirror-descent-based policy optimization algorithm for the general multi-turn preference-based RL problem, and prove its convergence to Nash equilibrium.