Margin-based strategies and model change based strategies represent two important types of strategies for active learning. While margin-based strategies have been dominant for Support Vector Machines (SVMs), most methods are based on heuristics and lack a solid theoretical support. In this paper, we propose an active learning strategy for SVMs based on Maximum Model Change (MMC). The model chan...

Stochastic gradient descent is popular for large scale optimization but has slow convergence asymptotically due to the inherent variance. To remedy this problem, we introduce an explicit variance reduction method for stochastic gradient descent which we call stochastic variance reduced gradient (SVRG). For smooth and strongly convex functions, we prove that this method enjoys the same fast conv...

Stochastic gradient descent algorithm (SGD) has been popular in various fields of artificial intelligence as well a prototype online learning algorithms. This article proposes novel and general framework one-sided testing for streaming data based on SGD, which determines whether the unknown parameter is greater than certain positive constant. We construct online-updated test statistic sequentia...

In this project, we approach the problem of English-word hyphenation using a linear-chain conditional random field model. We measure the effectiveness of different feature combinations and two different learning methods: Collins perceptron and stochastic gradient following. We achieve the accuracy rate of 77.95% using stochastic gradient descent.

Despite their popularity, the practical performance of asynchronous stochastic gradient descent methods (ASGD) for solving large scale machine learning problems are not as good as theoretical results indicate. We adopt and analyze a synchronous K-step averaging stochastic gradient descent algorithm which we call K-AVG. We establish the convergence results of KAVG for nonconvex objectives, and s...

We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past methods, such as stochastic gradient descent or online boosting, our approach estimates the weights by recursively updating its posterior distribution. For a specified class of loss functions, we show that it is possible to formulate a suitably defi...

Reinforcement learning by direct policy gradient estimation is attractive in theory but in practice leads to notoriously ill-behaved optimization problems. We improve its robustness and speed of convergence with stochastic meta-descent, a gain vector adaptation method that employs fast Hessian-vector products. In our experiments the resulting algorithms outperform previously employed online sto...

We present an algorithm for fast stochastic gradient descent that uses a nonlinear adaptive momentum scheme to optimize the late time convergence rate. The algorithm makes eeective use of curvature information, requires only O(n) storage and computation, and delivers convergence rates close to the theoretical optimum. We demonstrate the technique on linear and large nonlinear back-prop networks...

{ We analyse online (gradient descent) learning of a rule from a nite set of training examples at non-innnitesimal learning rates , calculating exactly the time-dependent generalization error for a simple model scenario. In the thermodynamic limit, we close the dynamical equation for the generating function of an innnite hierarchy of order parameters using`within-sample self-averaging'. The res...

Stochastic Gradient Descent (SGD) is arguably the most popular of the machine learning methods applied to training deep neural networks (DNN) today. It has recently been demonstrated that SGD can be statistically biased so that certain elements of the training set are learned more rapidly than others. In this article, we place SGD into a feedback loop whereby the probability of selection is pro...