A radically new approach to statistical modelling, which combines mathematical techniques
of Bayesian statistics with the philosophy of the theory of competitive on‐line algorithms, has …

We present new algorithms for optimizing non-smooth, non-convex stochastic objectives
based on a novel analysis technique. This improves the current best-known complexity for …

Online learning is a framework for the design and analysis of algorithms that build predictive
models by processing data one at the time. Besides being computationally efficient, online …

Wahba's classical representer theorem states that the solutions of certain risk minimization
problems involving an empirical risk term and a quadratic regularizer can be written as …

We consider on-line density estimation with a parameterized density from the exponential
family. The on-line algorithm receives one example at a time and maintains a parameter that …

We study on-line learning in the linear regression framework. Most of the performance
bounds for on-line algorithms in this framework assume a constant learning rate. To achieve …

We consider two on-line learning frameworks: binary classification through linear threshold
functions and linear regression. We study a family of on-line algorithms, called p-norm …

In most on-line learning research the total on-line loss of the algorithm is compared to the
total loss of the best off-line predictor u from a comparison class of predictors. We call such …

Abstract Concept drift occurs when a target concept changes over time. I present a new
method for learning shifting target concepts during concept drift. The method, called Concept …

We consider the AdaBoost procedure for boosting weak learners. In AdaBoost, a key step is
choosing a new distribution on the training examples based on the old distribution and the …