Wegiveanalgorithm thatoutputs anexplainable clustering that loses at most a factor ofO(log2k) compared to an optimal (not necessarily explainable) clustering for thek-medians objective, and a factor of O(klog2k)forthek-meansobjective.

This paper presents a text classification algorithm inspired by the notion of superposition of states in quantum physics.

According to our experimental findings, inaccurate cross-attention maps are at the root of this problem.

Neural Information Processing Systems http://nips.cc/

On both synthetic and real data, we empirically show that our proposed algorithm is able to learn better CVaRoptimizedpolicies.

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas.

Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has been a lot of recent progress in understanding the convergence of Gradient Flow (GF) on the population loss, partly due to the simplicity thatacontinuous-time analysis buysus.