|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Distributed, Private and Bayesian Machine Learning
If you have a question about this talk, please contact lecturescam.
Please be aware that this event may be recorded. Microsoft will own the copyright of any recording and reserves the right to distribute it as required.
Data and compute are resources distributed at different locations around the globe. To reap the full benefits of these essential ingredients for machine learning we need to develop algorithms that operate distributed, are privacy preserving by design and treat model uncertainty in a principled manner. In this talk I will discuss the progress that we have made at AMLAB and QUVA Lab towards these goals. I will in particular discuss an interesting synergy that seems to exists between these three goals.
This talk is part of the Microsoft Research Cambridge, public talks series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsCentre for Science and Policy Seminars Clare College Student Investment Fund Historical Linguistics Research Cluster
Other talksmen need help too Lies, Damn'd lies and statistics: why it is (almost) impossible to communicate risk ethically Postgraduate Diploma in Entrepreneurship webinar The use of model based adaptive dose response in choosing doses in a lean clinical development plan Calabi-Yau volumes and Reflexive Polytopes Homotopy theory with C*-categories