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 …

In the first part of the paper we consider the problem of dynamically apportioning resources
among a set of options in a worst-case on-line framework. The model we study can be …

We consider the problem of dynamically apportioning resources among a set of options in a
worst-case on-line framework. The model we study can be interpreted as a broad, abstract …

We consider two algorithms for on-line prediction based on a linear model. The algorithms
are the well-known gradient descent (GD) algorithm and a new algorithm, which we call …

We analyze algorithms that predict a binary value by combining the predictions of several
prediction strategies, called experts. Our analysis is for worst-case situations, ie, we make no …

We generalize the recent relative loss bounds for on-line algorithms where the additional
loss of the algorithm on the whole sequence of examples over the loss of the best expert is …

This paper introduces a novel optimization method for differential neural architecture search,
based on the theory of prediction with expert advice. Its optimization criterion is well fitted for …

We consider the following situation. At each point of discrete time the learner must make a
prediction; he is given the predictions made by a pool of experts. Each prediction and the …

At each point in time a decision maker must make a decision. The payoff in a period from the
decision made depends on the decision as well as on the state of the world that obtains at …

We study online learning algorithms that predict by combining the predictions of severrd
subordinate prediction algorithms, sometimes crdled “experts.“These simple algorithms …