Sampling process

Thursday, December 8, 2005 - 3:00pm - 4:00pm
Robert Nowak (University of Wisconsin, Madison)
Adaptive sampling, also called Active Learning, uses information
gleaned from previous measurements (e.g., feedback) to guide and focus
the sampling process. Theoretical and experimental results have shown
that adaptive sampling can dramatically outperform conventional
non-adaptive sampling schemes. I will review some of the most
encouraging theoretical results to date, and focus on new results
regarding the capabilities of adaptive sampling methods for learning
piecewise smooth functions. I will also contrast adaptive sampling
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