138,498 research outputs found
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
This work takes a critical look at the application of conventional machine
learning methods to wireless communication problems through the lens of
reliability and robustness. Deep learning techniques adopt a frequentist
framework, and are known to provide poorly calibrated decisions that do not
reproduce the true uncertainty caused by limitations in the size of the
training data. Bayesian learning, while in principle capable of addressing this
shortcoming, is in practice impaired by model misspecification and by the
presence of outliers. Both problems are pervasive in wireless communication
settings, in which the capacity of machine learning models is subject to
resource constraints and training data is affected by noise and interference.
In this context, we explore the application of the framework of robust Bayesian
learning. After a tutorial-style introduction to robust Bayesian learning, we
showcase the merits of robust Bayesian learning on several important wireless
communication problems in terms of accuracy, calibration, and robustness to
outliers and misspecification.Comment: Submitted for publicatio
New results about multi-band uncertainty in Robust Optimization
"The Price of Robustness" by Bertsimas and Sim represented a breakthrough in
the development of a tractable robust counterpart of Linear Programming
Problems. However, the central modeling assumption that the deviation band of
each uncertain parameter is single may be too limitative in practice:
experience indeed suggests that the deviations distribute also internally to
the single band, so that getting a higher resolution by partitioning the band
into multiple sub-bands seems advisable. The critical aim of our work is to
close the knowledge gap about the adoption of a multi-band uncertainty set in
Robust Optimization: a general definition and intensive theoretical study of a
multi-band model are actually still missing. Our new developments have been
also strongly inspired and encouraged by our industrial partners, which have
been interested in getting a better modeling of arbitrary distributions, built
on historical data of the uncertainty affecting the considered real-world
problems. In this paper, we study the robust counterpart of a Linear
Programming Problem with uncertain coefficient matrix, when a multi-band
uncertainty set is considered. We first show that the robust counterpart
corresponds to a compact LP formulation. Then we investigate the problem of
separating cuts imposing robustness and we show that the separation can be
efficiently operated by solving a min-cost flow problem. Finally, we test the
performance of our new approach to Robust Optimization on realistic instances
of a Wireless Network Design Problem subject to uncertainty.Comment: 15 pages. The present paper is a revised version of the one appeared
in the Proceedings of SEA 201
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