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How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses.
On a measure of the information provided by an experiment
D. V. Lindley · 1956
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Information value theory
R. A. Howard · 1966
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An experimental system for computer controlled mechanical assembly
P. M. Will and D. D. Grossman · 1975
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Bayesian experimental design: A review
K. Chaloner and I. Verdinelli · 1995
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Active markov localization for mobile robots
D. Fox, W. Burgard, and S. Thrun · 1998
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Symmetric Functions and Hall Polynomials
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On an optimal split tree problem
R. S. Kosaraju and T. M. P. A. R. S. Borgstrom · 1999
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Noisy generalized binary search
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Near-optimal bayesian active learning with noisy observations
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Adaptive submodularity: Theory and applications in active learning and stochastic optimization
D. Golovin and A. Krause · 2011
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Comparison-based learning with rank nets
A. Karbasi, S. Ioannidis, and L. Massoulie · 2012
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Robust belief-based execution of manipulation programs
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Efficient touch based localization through submodularity
S. Javdani, M. Klingensmith, J. A. D. Bagnell, N. Pollard, and S. Srinivasa · 2013
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