Bayesian Optimization and Self Driving Cars


Jeff Schneider, Carnegie Mellon/Uber

Bayesian Optimization and Self Driving Cars

4-5 pm 240 Bechtel, 3:30 cookies and beverages ITS Liibrary

Abstract: An important property of embedded learning systems is the ever-changing environment they create for all algorithms operating in the system. Optimizing the performance of those algorithms becomes a perpetual online activity rather than a one-off task. I will review some of these challenges in autonomous vehicles. I will discuss active optimization methods and their application in robotics and scientific applications, focusing on scaling up the dimensionality and managing multi-fidelity evaluations. I will finish with lessons learned and thoughts on future directions as we strive to bring autonomous vehicles into widespread use.

Bio: Dr. Jeff Schneider is the engineering lead for machine learning at Uber's Advanced Technologies Center. He is also a research professor in the Carnegie Mellon University School of Computer Science. He has 20 years experience developing, publishing, and applying machine learning algorithms in government, science, and industry. He has more than 100 publications and regularly gives talks and tutorials on the subject.