Stanford Robotics Seminar: Probabilistic Numerics — Uncertainty in Computation, Philipp Hennig, Max Plank Institute for Intelligent Systems

Stanford Robotics Seminar<br><br>Title: Probabilistic Numerics — Uncertainty in Computation<br>Speaker: <a href="https://www.google.com/url?q=https%3A%2F%2Fpn.is.tuebingen.mpg.de%2Fpers... target="_blank">Philipp Hennig</a>, Max Plank Institute for Intelligent Systems<br>Date: December 04, 2017<br>Time: 12:00pm<br>Location: Packard 202<br><br>Abstract: <br>The computational complexity of inference from data is dominated by the solution of non-analytic numerical problems (large-scale linear algebra, optimization, integration, the solution of differential equations). But a converse of sorts is also true — numerical algorithms for these tasks are inference engines! They estimate intractable, latent quantities by collecting the observable result of tractable computations. Because they also decide adaptively which computations to perform, these methods can be interpreted as autonomous inference agents. This observation lies at the heart of the emerging topic of Probabilistic Numerical Computation, which applies the concepts of probabilistic (Bayesian) inference to the design of algorithms, assigning a notion of probabilistic uncertainty to the result even of deterministic computations. I will outline how this viewpoint is connected to that of classic numerical analysis, and show that thinking about computation as inference affords novel, practical answers to the challenges of large-scale, big data, inference.&nbsp;<br><br>Bio:&nbsp;<br>Philipp Hennig studied Physics in Heidelberg and London, and received his PhD from the University of Cambridge, UK, in 2011. He now runs an independent research group at to the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His group develops numerical algorithms both for and as intelligent, autonomous systems. It has been influential in the emergence of the research area of probabilistic numerical methods. Hennig works primarily in the machine learning community, but also has ties to applied mathematics, control engineering, and statistics.<br>

Date: 
Monday, December 4, 2017 - 12:00pm to 1:00pm
location: 
Packard 202