University of Cambridge > > Microsoft Research Machine Learning and Perception Seminars > Sequential Decision Making in Experimental Design and Sustainability via Adaptive Submodularity

Sequential Decision Making in Experimental Design and Sustainability via Adaptive Submodularity

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.

This event may be recorded and made available internally or externally via Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending

Solving sequential decision problems under partial observability is a fundamental but notoriously difficult challenge. I will introduce the new concept of adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. We prove that, if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. The concept allows us to recover, generalize, and extend existing results in diverse applications, including sensor management, viral marketing, and active learning. I will focus on two case studies. In an application to Bayesian experimental design in Behavioral Economics, we show how greedy optimization of a novel adaptive submodular criterion outperforms standard myopic techniques based on information gain and value of information. I will also discuss how adaptive submodularity can help to address problems in computational sustainability by presenting results on conservation planning for three rare species in the Pacific Northwest of the United States.

This talk is based on joint work primarily with Daniel Golovin

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2022, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity