Bayesian optimization explains human active search Bayesian optimization explains human active search
Paper summary The authors explore different optimization strategies for 1-D continuous functions and their relationship to how people optimize the functions. They used a wide variety of continuous functions (with one exception): polynomial, exponential, trigonometric, and the Dirac function. They also explore how people interpolate and extrapolate noisy samples from a latent function (which has a long tradition in psychology under the name of function learning) and how people select an additional sample to observe under the task of interpolating or extrapolating. Over all, they found that Gaussian processes do a better job at describing human performance than any of the approx. 20 other tested optimization methods.
Bayesian optimization explains human active search
Borji, Ali and Itti, Laurent
Neural Information Processing Systems Conference - 2013 via Local Bibsonomy
Keywords: dblp

Summary by NIPS Conference Reviews 2 years ago
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