We are pleased to announce that we have secured Professor Alfred Hero from the University of Michigan as one of our keynote speakers for SSPD2019.
His keynote is titled Learning to Benchmark.
Using mathematical models to benchmark the capability of a sensor platform to provide data for accurate signal detection, classification, or estimation has been an essential part of performance-driven system design. When a mathematical model is unreliable, or not available, a natural question to ask is whether it is possible to use machine learning to accurately benchmark the capability of a sensor solely from experimental data collected from the sensor. In this talk we will answer this question in the affirmative. For example, in the context of classification, empirical estimation of the minimal achievable classification error, i.e., the Bayes error rate, from labeled experimental sensor data can be framed as the meta-learning problem of estimating the Bayes-optimal misclassification error rate without having to estimate the Bayes-optimal classifier. The talk will cover relevant background, theory, algorithms, and applications of benchmark learning.