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DTSTART:20190331T030000
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DTSTAMP:20260405T232504Z
UID:5c594f34b8dd8802140041@ist.ac.at
DTSTART:20190207T103000
DTEND:20190207T113000
DESCRIPTION:Speaker: Emir Demirovic\nhosted by Vladimir Kolmogorov\nAbstrac
 t: In the first part of the talk\, we introduce the Predict+Optimise probl
 em setting and discuss developing machine learning algorithms that are spe
 cifically designed to be used with combinatorial problems. These algorithm
 s are important when machine learning and optimisation must interact. For 
 instance\, when optimisation is to be performed on data that is not entire
 ly correct but is rather estimated with machine learning. The main challen
 ge is that conventional machine learning metrics\, such as mean-square err
 or\, are not necessarily indicative of the outcome of optimisation procedu
 re. Ideally\, the latter would be used as a criterion of success. However\
 , not only does this involve solving an (NP-hard) optimisation problem\, b
 ut in addition\, this gives riseto a nonlinear\, noncontinuous\, and nondi
 fferentiable function. Hence\, techniques widely used in machine learning\
 , e.g. gradient descent\, cannot be applied. The standard approach is to v
 iew optimisation and machine learning as two separate black boxes\, but po
 tentially better results can be obtained if the process is merged into a s
 ingle pipeline. Although both combinatorial optimisation and machine learn
 ing have been thoroughly studied\, their interplay has yet to be understoo
 d. In this talk\, we present ourformalisation of the Predict+Optimise prob
 lem and its challenges\, discuss various approaches\, and provide an exper
 imental study.The second part of the talk is devoted to the maximum Boolea
 n satisfiability problem (MaxSAT)\, where the aim is to compute an assignm
 ent of variables that satisfy as many clauses as possible for a propositio
 nal logic formula. Many real-life problems can be formulated in propositio
 nal logic and thus developing algorithms for this problem is of high impor
 tance. Given that practical applications can lead to large formulas that n
 eed to be solved with tight time budgets\, we focus our attention on metho
 ds that provide 'good' solutions 'quickly\; We developed techniques for Ma
 xSAT solving based on the exhaustive linear search algorithm and utilise t
 echniques inspired by local search. The resulting algorithm proved to be h
 ighly effective\, taking first place in the 300 seconds incomplete track o
 f the MaxSAT Evaluation 2018. Moreover\, we briefly discuss further improv
 ements based on the integration of the algoithm with a core-guided approac
 h\, essentially merging lower and upper bounding techniques.Dr Emir Demiro
 vi? is an associate lecturer and postdoctoral researcher at the University
  of Melbourne in Australia\, working closely with Professors Peter J. Stuc
 key\, James Bailey\, Rao Kotagiri\, Christopher Leckie\, and Dr Jeffrey Ch
 an. His main expertise lies in solving combinatorial optimisation problems
  through the use of constraint/integer programming\, metaheuristics\, and 
 the combination thereof\, with special attention to timetabling and schedu
 ling algorithms. After earning his Ph.D. in 2017 at TU Wien under Priv.-Do
 z. Dr. Nysret Musliu supervision\, Dr Demirovi? worked as an optimisation 
 expert in the industry prior to coming to Australia. Throughout his career
 \, he maintained a strong collaboration with the National Institute of Inf
 ormatics and the National Institute of Advanced Industrial Science and Tec
 hnology in Tokyo\, where he was invited several times. More information ca
 n be found on his personal website:www.emirdemirovic.com.
LOCATION:Meeting room 3rd floor / Central Bldg. (I01.3OG.Meeting Room)\, IS
 TA
ORGANIZER:abonvent@ist.ac.at
SUMMARY:Emir Demirovic: Combinatorial Machine Learning and Incomplete MaxSA
 T Solving
URL:https://talks-calendar.ista.ac.at/events/1796
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