… to distiguish small-scale and large-scale learning problems. In the small-scale case, we
recover the classical tradeoff between approximation and estimation. The large-scale case is …

… to distiguish small-scale and large-scale learning problems. In the small-scale case, we recover
… Estimation–Optimization tradeoff (3) for large scale problems satisfying our assumptions. …

… Such large scale systems have been designed and industrially deployed with
considerable success. … The discussion so far has established that a properly designed …

… Then we describe how to organize feasible direction searches into an online learning … Our
final approach simply considers a huge virtual training set composed of all examples and all …

We describe a vision-based obstacle avoidance system for off-road mobile robots. The
system is trained from end to end to map raw in put images to steering angles. It is trained in …

… Large Scale Online Learning Léon Bottou, Yann Le Cun … Online Classification on a Budget …

… Stochastic optimization finds wide application in signal processing, online learning, and
network problems, especially problems process ing large-scale data. We propose an …

… , we propose to utilize online learning algorithms such that the models can adapt their
parameters to the network dynamics without the overhead of data collection and training. …

Lush is an object-oriented programming language with features designed to please
researchers, experimenters, and engineers interested in large-scale numerical and graphical …

… Figure 2: Large scale linear classification with log loss on four … to class CCAT in the text
dataset (Lewis et al., 2004), where we … Adaptive subgradient methods for online learning and …