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PRODID:-//DSO - EURO Working Group on Data Science meets Optimization - ECPv6.18.0//NONSGML v1.0//EN
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X-WR-CALNAME:DSO - EURO Working Group on Data Science meets Optimization
X-ORIGINAL-URL:https://www.euro-online.org/websites/dso
X-WR-CALDESC:Events for DSO - EURO Working Group on Data Science meets Optimization
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DTSTART:20160101T000000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20170717
DTEND;VALUE=DATE:20170722
DTSTAMP:20181231T142004Z
CREATED:20170331T131042Z
LAST-MODIFIED:20181231T142004Z
UID:223-1500249600-1500681599@www.euro-online.org
SUMMARY:DSO Stream at IFORS 2017
DESCRIPTION:Data Science meets Optimisation Streams 2017 \n\n\n\nCall for Abstracts \nStream on Data Science meets Optimisation \nQuebec city\, Quebec\, Canada  (July 17-21\, 2017) \nScope: \nThe interaction of data science (DS) and optimisation (O) is the central theme of the working group (DSO). DSO originates from the observation that\, on the one hand\, real time optimisation algorithms are tightly linked to the data-context\, and on the other hand\, many data-analytic algorithms rely on optimisation algorithms\, while many modern optimisation algorithms have some form of machine learning embedded. The first observation has a.o. led to developments in automated algorithm tuning\, configuration and construction to adapt or even create algorithms from a historical body of data.   The second observation is cause to development of similar ideas in different contexts but without much interaction. It is the aim of the working group to bring the two domains closer to each to better contribute to the aims of EURO\, the European Organisation for Operations Research. \nCo-located with \n21st  conference of international federation of operations research societies (IFORS 2017) \nQuebec city\, Quebec\, Canada  (July 17-21\, 2017)
URL:https://www.euro-online.org/websites/dso/event/test-all-day/
LOCATION:Quebec city\,\, Canada
CATEGORIES:Conferences
ATTACH;FMTTYPE=image/jpeg:https://www.euro-online.org/websites/dso/wp-content/uploads/sites/17/2016/12/slide3.jpg
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20170605
DTEND;VALUE=DATE:20170606
DTSTAMP:20181231T143243Z
CREATED:20170216T203252Z
LAST-MODIFIED:20181231T143243Z
UID:172-1496620800-1496707199@www.euro-online.org
SUMMARY:DSO workshop: CEC 2017 & CPAIOR 2017
DESCRIPTION:DSO workshop colocated with CEC 2017 and CPAIOR 2017\nINVITED SPEAKERS: \n\nOn the role of (machine) learning in (mathematical) optimization\nAndrea Lodi\n\nIn this talk\, I try to explain my point of view as a Mathematical Optimizer — especially concerned with discrete (integer) decisions — on Big Data. I advocate a tight integration of Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science. For such an integration I concentrate on three questions: 1) what can optimization do for machine learning? 2) what can machine learning do for optimization? 3) which new applications can be solved by the combination of machine learning and optimization? Finally\, I will discuss in details two areas in which machine learning techniques have been (successfully) applied in the area of mixed-integer programming. [PDF] \n\nRelational Quadratic Programming: Exploiting Symmetries for Modelling and Solving Quadratic Programs\nKristian Kersting\n\nSymmetry is the essential element of lifted inference that has recently demonstrated the possibility to perform very efficient inference in highly-connected\, but symmetric probabilistic models models aka. relational probabilistic models. This raises the question\, whether this holds for optimization problems in general. In this talk I shall demonstrate that for a large class of mathematical programs this is actually the case. More precisely\, I shall introduce the concept of fractional symmetries of linear and convex quadratic programs (QPs)\, which lie at the heart of many machine learning approaches\, and exploit it to lift\, i.e.\, to compress them. These lifted QPs can then be tackled with the usual optimization toolbox (off-the-shelf solvers\, cutting plane algorithms\, stochastic gradients etc.): If the original QP exhibits symmetry\, then the lifted one will generally be more compact\, and hence their optimization is likely to be more efficient.[PDF] \nThis talk is based on joint works with Martin Mladenov\, Martin Grohe\, Leonard Kleinhans\, Pavel Tokmakov\, Babak Ahmadi\, Amir Globerson\, and many others.
URL:https://www.euro-online.org/websites/dso/event/event-1/
CATEGORIES:Conferences
ATTACH;FMTTYPE=image/jpeg:https://www.euro-online.org/websites/dso/wp-content/uploads/sites/17/2016/12/slide3.jpg
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