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When applying aggregating strategies to Prediction with Expert Advice , the learning rate must be adaptively tuned. The natural choice of complexity/current loss renders the analysis of Weighted Majority derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. The analysis of the alternative " Follow the(More)
When applying aggregating strategies to Prediction with Expert Advice , the learning rate must be adaptively tuned. The natural choice of complexity/current loss renders the analysis of Weighted Majority derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. The analysis of the alternative " Follow the(More)
Switzerland (SUPSI), and was founded in 1988 by the Dalle Molle Foundation which promoted quality of life. Abstract Analyzing the affinity matrix spectrum is an increasingly popular data clustering method. We propose three new algorithmic components which are appropriate for improving performance of spectral clustering. First, observing the eigenvectors(More)
A main problem of " Follow the Perturbed Leader " strategies for online decision problems is that regret bounds are typically proven against oblivious adversary. In partial observation cases, it was not clear how to obtain performance guarantees against adaptive adversary, without worsening the bounds. We propose a conceptually simple argument to resolve(More)
Minimum description length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning processes which are independent and identically distributed (i.i.d.) by means of two-part MDL, where the underlying model class is countable. We consider the online learning framework,(More)
This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedback from the actions actually chosen (bandit setup), (b) it can be applied with countably infinite expert classes, and (c) it copes with losses that may grow in time appropriately(More)
The covariance matrix adaptation (CMA) is one of the most powerful self adaptation mechanisms for Evolution Strategies. However, for increasing search space dimension N , the performance declines, since the CMA has space and time complexity O(N 2). Adapting the main mutation vector instead of the covariance matrix yields an adaptation mechanism with space(More)