We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true dis...
We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. We derive upper and lower relative loss bounds for a cla...
In this work we consider the problem of universal prediction of individual sequences where the universal predictor is a deterministic finite state machine, with a fixed, relativel...
We consider sequential regression of individual sequences under the square error loss. Using a competitive algorithm framework, we construct a sequential algorithm that can achieve...