[Statlist] Next talk: Friday, 20.09.2019, with Guillaume Obozinski, Swiss Data Science Center

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talk:

Friday, 20.09.2019, at 15.15 h  ETH Zurich, HG G19.1
with Guillaume Obozinski, Swiss Data Science Center

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Title:

Integer programming and linear programming relaxation on the junction tree polytope for Influence Diagrams <https://math.ethz.ch/sfs/news-and-events/research-seminar.html?s=hs19#e_13925>

Abstract:

Influence Diagrams (ID) provide a flexible framework to represent discrete stochastic optimization problems, including Markov Decision Process (MDP) and Partially Observable MDP as standard examples. In Influence Diagrams, the random variables are associated with a probabilistic graphical model whose vertices are partitioned into three types : chance, decision and utility vertices. The user has to choose the distribution of the decision vertices conditionally to their parents in order to maximize the expected utility. Leveraging a notion of rooted junction tree that we introduced with collaborators, I will show how the maximum expected utility problem on an influence diagram can be reformulated advantageously as a mixed integer linear problem on the marginal polytope of this junction tree. Then I will propose a way to obtain a good LP relaxation by identifying maximal sets that are invariant under the choice of the policy in the sense of the literature on causality. These LP relaxations allow for more efficient branch-and-bound algorithms but could also have other applications.


This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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