We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypot...
Abstract—We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated ob...
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments a...
In most real-world situations, a single microphone is insufficient for the characterization of an entire auditory scene. This often occurs in places such as office environments ...
Gordon Wichern, Harvey D. Thornburg, Andreas Spani...
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...