Author Archives: Herke van Hoof

Virtual talk by Jens Kober on Robots Learning (Through) Interactions

Following RIVM guidelines, we will host a completely virtual seminar in the Delta Lab Deep Learning Seminar Series. We will livestream the talk at the brand-new AMLab YouTube channel, starting May 7th at 11:00 CEST:
https://www.youtube.com/channel/UC-UamuSbKi_Dcaa4wlEyqlA
(Note: in we need to change the streaming link because of technical problems, look for updates here)

Abstract:
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Reinforcement learning and imitation learning are two different but complimentary machine learning approaches commonly used for learning motor skills.
In this seminar, Jens Kober will discuss various learning techniques we developed that can handle complex interactions with the environment. Complexity arises from non-linear dynamics in general and contacts in particular, taking multiple reference frames into account, dealing with high-dimensional input data, interacting with humans, etc. A human teacher is always involved in the learning process, either directly (providing demonstrations) or indirectly (designing the optimization criterion), which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective?
All these concepts will be illustrated with benchmark tasks and real robot experiments ranging from fun (ball-in-a-cup) to more applied (unscrewing light bulbs).

Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.

Talk by David Blei on The Blessings of Multiple Causes

You are all cordially invited to the UvA-Bosch Delta lab seminar on Thursday October 17th October at 15:00 on the Roeterseilandcampus A2.11 , where  David Blei, well known for his fantastic work on LDA, Bayesian nonparametrics, and variational inference. He will give a talk on “The Blessings of Multiple Causes”.

Abstract:

Causal inference from observational data is a vital problem, but itcomes with strong assumptions. Most methods require that we observeall confounders, variables that affect both the causal variables andthe outcome variables. But whether we have observed all confounders isa famously untestable assumption. We describe the deconfounder, a wayto do causal inference with weaker assumptions than the classicalmethods require.
How does the deconfounder work? While traditional causal methodsmeasure the effect of a single cause on an outcome, many modernscientific studies involve multiple causes, different variables whoseeffects are simultaneously of interest. The deconfounder uses thecorrelation among multiple causes as evidence for unobservedconfounders, combining unsupervised machine learning and predictivemodel checking to perform causal inference.  We demonstrate thedeconfounder on real-world data and simulation studies, and describethe theoretical requirements for the deconfounder to provide unbiasedcausal estimates.
This is joint work with Yixin Wang.
[*] https://arxiv.org/abs/1805.06826


Biography


David Blei is a Professor of Statistics and Computer Science atColumbia University, and a member of the Columbia Data ScienceInstitute. He studies probabilistic machine learning, including itstheory, algorithms, and application. David has received several awardsfor his research, including a Sloan Fellowship (2010), Office of NavalResearch Young Investigator Award (2011), Presidential Early CareerAward for Scientists and Engineers (2011), Blavatnik Faculty Award(2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship(2017), and a Simons Investigator Award (2019). He is theco-editor-in-chief of the Journal of Machine Learning Research.  He isa fellow of the ACM and the IMS.