Self-Avoiding Random Dynamics on Integer Complex Systems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Active learning of intuitive control knobs for synthesizers using gaussian processes
Proceedings of the 19th international conference on Intelligent User Interfaces
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We describe a Bayesian formalism for the intelligent selection of observations from sensor networks that may intermittently undergo faults or changepoints. Such active data selection is performed with the goal of taking as few observations as necessary in order to maintain a reasonable level of uncertainty about the variables of interest. The presence of faults/changepoints is not always obvious and therefore our algorithm must first detect their occurrence. Having done so, our selection of observations must be appropriately altered. Faults corrupt our observations, reducing their impact; changepoints (abrupt changes in the characteristics of data) may require the transition to an entirely different sampling schedule. Our solution is to employ a Gaussian process formalism that allows for sequential time-series prediction about variables of interest along with a decision theoretic approach to the problem of selecting observations.