Active Learning, Distilled Sensing, or how to close the loop between data analysis and acquisition
Speaker: Rui Castro, Assistant Professor , Columbia University, Dept of Electrical Engineering
Abstract: Many traditional approaches to statistical inference and machine learning are passive, in the sense that all data are collected prior to analysis in a non-adaptive fashion. However, in many practical scenarios it is possible to adjust the data collection process based on information gleaned from previous observations, in the spirit of the "twenty-questions" game. Learning in such settings is known as active learning or inference using sequential experimental designs. Despite the potential to dramatically improve inference performance, analysis of such procedures is difficult, due to the complicated data dependencies created by the closed-loop observation process. These difficulties are further exasperated by the presence of measurement uncertainty or noise.