… of offline (also called multi-pass) SGD exhibits ‘approximate’ power-law tails and the
approximation … Our main takeaway is that, as the number of data points increases, offline SGD will …

… of offline (also called multi-pass) SGD exhibits ‘approximate’ power-law tails and the
approximation … Our main takeaway is that, as the number of data points increases, offline SGD will …

… of offline (also called multi-pass) SGD exhibits 'approximate' power-law tails and the
approximation … Our main takeaway is that, as the number of data points increases, offline SGD will …

… and benefits of analyzing multi-pass training, we present a … , we show that “stochastic gradient
noise” during optimization is … rank-1 approximation matrix A to state the following lemma. …

… an (approximate) sample median is robust to heavy-tailed noise, … That is, we sample one
stochastic gradient gt per iteration … heavy tails in offline (multi-pass) stochastic gradient descent. …

… Recently, several studies have provided empirical and theoretical evidence that stochastic
gradient descent (SGD) can exhibit heavy tails when the step-size is chosen large, or the …

… α close to 1, very heavy tails distributions may not verify the … used within an offline stochastic
gradient descent (SGD) … work on convergence guarantees for offline multi-pass SGD [41, 42, …

… Algorithmic stability of heavytailed stochastic gradient descent on least squares. In …
However, ρα remains mainly theoretical, as it is the continuous approximation of a discrete …

… This paper explores the more complicated setting of offline training of neural networks (in …
to use stochastic gradient descent (SGD), which only requires access to the gradient of the …

… categorized into three major groups of offline, online and real-time, as shown in Figure I.3. …
A commonly used optimizer is mini-batch stochastic gradient descent, and it can be explained …