52,456 research outputs found
Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet
Various optimality properties of universal sequence predictors based on
Bayes-mixtures in general, and Solomonoff's prediction scheme in particular,
will be studied. The probability of observing at time , given past
observations can be computed with the chain rule if the true
generating distribution of the sequences is known. If
is unknown, but known to belong to a countable or continuous class \M
one can base ones prediction on the Bayes-mixture defined as a
-weighted sum or integral of distributions \nu\in\M. The cumulative
expected loss of the Bayes-optimal universal prediction scheme based on
is shown to be close to the loss of the Bayes-optimal, but infeasible
prediction scheme based on . We show that the bounds are tight and that no
other predictor can lead to significantly smaller bounds. Furthermore, for
various performance measures, we show Pareto-optimality of and give an
Occam's razor argument that the choice for the weights
is optimal, where is the length of the shortest program describing
. The results are applied to games of chance, defined as a sequence of
bets, observations, and rewards. The prediction schemes (and bounds) are
compared to the popular predictors based on expert advice. Extensions to
infinite alphabets, partial, delayed and probabilistic prediction,
classification, and more active systems are briefly discussed.Comment: 34 page
On the Combinatorial Version of the Slepian-Wolf Problem
We study the following combinatorial version of the Slepian-Wolf coding
scheme. Two isolated Senders are given binary strings and respectively;
the length of each string is equal to , and the Hamming distance between the
strings is at most . The Senders compress their strings and
communicate the results to the Receiver. Then the Receiver must reconstruct
both strings and . The aim is to minimise the lengths of the transmitted
messages.
For an asymmetric variant of this problem (where one of the Senders transmits
the input string to the Receiver without compression) with deterministic
encoding a nontrivial lower bound was found by A.Orlitsky and K.Viswanathany.
In our paper we prove a new lower bound for the schemes with syndrome coding,
where at least one of the Senders uses linear encoding of the input string.
For the combinatorial Slepian-Wolf problem with randomized encoding the
theoretical optimum of communication complexity was recently found by the first
author, though effective protocols with optimal lengths of messages remained
unknown. We close this gap and present a polynomial time randomized protocol
that achieves the optimal communication complexity.Comment: 20 pages, 14 figures. Accepted to IEEE Transactions on Information
Theory (June 2018
A Comparison between Fixed-Basis and Variable-Basis Schemes for Function Approximation and Functional Optimization
Fixed-basis and variable-basis approximation schemes are compared for the problems of function approximation and functional optimization (also known as infinite programming). Classes of problems are investigated for which variable-basis schemes with sigmoidal computational
units perform better than fixed-basis ones, in terms of the minimum number of computational units needed to achieve a desired error in function approximation or approximate optimization. Previously known bounds on the accuracy are extended, with better rates, to families o
Democratic Representations
Minimization of the (or maximum) norm subject to a constraint
that imposes consistency to an underdetermined system of linear equations finds
use in a large number of practical applications, including vector quantization,
approximate nearest neighbor search, peak-to-average power ratio (or "crest
factor") reduction in communication systems, and peak force minimization in
robotics and control. This paper analyzes the fundamental properties of signal
representations obtained by solving such a convex optimization problem. We
develop bounds on the maximum magnitude of such representations using the
uncertainty principle (UP) introduced by Lyubarskii and Vershynin, and study
the efficacy of -norm-based dynamic range reduction. Our
analysis shows that matrices satisfying the UP, such as randomly subsampled
Fourier or i.i.d. Gaussian matrices, enable the computation of what we call
democratic representations, whose entries all have small and similar magnitude,
as well as low dynamic range. To compute democratic representations at low
computational complexity, we present two new, efficient convex optimization
algorithms. We finally demonstrate the efficacy of democratic representations
for dynamic range reduction in a DVB-T2-based broadcast system.Comment: Submitted to a Journa
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