Random Reshuffling (RR) is an algorithm for minimizing finite-sum functions that utilizes iterative gradient descent steps in conjunction with data reshuffling. Often contrasted with its sibling Stochastic Gradient Descent (SGD), RR is usually faster in practice and enjoys significant popularity in convex and non-convex optimization. The convergence rate of RR has attracted substantial attention recently and, for strongly convex and smooth functions, it was shown to converge faster than SGD if 1) the stepsize is small, 2) the gradients are bounded, and 3) the number of epochs is large.

An important question arises regarding whether there is a fundamental trade-off between reproducibility and convergence speed in algorithms.

Query embedding (QE)--which aims to embed entities and first-order logical (FOL) queries inlow-dimensional spaces--has showngreatpowerinmulti-hop reasoningoverknowledgegraphs.

Clustering models constitute aclass of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in moderndatascience.

Data augmentation plays a key role in modern machine learning pipelines.