Moreover, our robust coreset can be efficiently maintained in fullydynamic environment. To the best of our knowledge, this is the first robust and fully-dynamic coreset construction method for these optimization problems.

However,GNNs have notconsidered different types ofuncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source uncertainty framework using a GNN that reflects various types of predictive uncertainties in both deep learning and belief/evidence theory domains fornodeclassification predictions.

Byconstruction0 < h h < 1 implies q h(x) q h(x). Wealsoacknowledgesupportofthe European Research Council (grant SEQUOIA 724063).

We present Deep Region Competition (DRC), an algorithm designed to extract foreground objects from images in a fully unsupervised manner.

The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness.

Step decay step-size schedules (constant and then cut) are widely used in practice because of their excellent convergence and generalization qualities, but their theoretical properties are not yet well understood. Weprovide convergence results for step decay in the non-convexregime, ensuring that the gradient norm vanishes at an O(lnT/ T)rate.