Oza’s Online Boosting algorithm provides a version of AdaBoost which can be trained in an online way for stationary problems. One perspective is that this enables the power of the boosting framework to be applied to datasets which are too large to fit into memory. The online boosting algorithm assumes the data distribution to be independent and identically distributed (i.i.d.) and therefore has...

1 Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences. Consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most...

The existence of shift for periodically correlated processes and its boundedness are investigated. Spectral criteria for these non-stationary processes to have such shifts are obtained.

We consider a non-stationary variant of a sequential stochastic optimization problem, in which the underlying cost functions may change along the horizon. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance. We identify sharp conditions under which it is possible to achieve long-run avera...

We propose non-stationary spectral kernels for Gaussian process regression. Wepropose to model the spectral density of a non-stationary kernel function as amixture of input-dependent Gaussian process frequency density surfaces. Wesolve the generalised Fourier transform with such a model, and present a familyof non-stationary and non-monotonic kernels that can learn input-depende...

This paper studies repeated interactions between an agent and an unknown opponent that changes its strategy over time. We propose a framework for learning switching nonstationary strategies. The approach uses decision trees to learn the most up to date opponent’s strategy. Then, the agent’s strategy is computed by transforming the tree into a Markov Decision Process (MDP), whose solution dictat...

Analyzing datasets, such as sea-level records, pose a challenging statistical problem for reasons including non-stationarity, non-uniformly smooth spatial boundaries, and sparsity in the data. In this paper, we propose a framework to estimate the non-stationary covariance function by employing intrinsic statistics on the local covariates. These local covariates represent the underlying local co...

Abstract Australian summer heat events have become more frequent and severe in recent times. Temperature-duration-frequency (TDF) curves connect the severity of episodes various durations to their frequencies thus can be an effective tool for analysing extremes. This study examines using data from 82 meteorological stations. TDF been developed under stationary non-stationary conditions. General...