Discrete Optimization Talks (DOTs)
DOTs are virtual discrete optimization talks, organized by Aleksandr M. Kazachkov and Elias B. Khalil. To receive updates (and the Zoom link) for upcoming DOTs, please join our mailing list. If you would like to give a DOT, please fill out this form or email us.
Topics of interest include theoretical, computational, and applied aspects of integer and combinatorial optimization.
The format is two 30-minute talks each session. From January to April 2021, the seminars are scheduled tentatively for the last Friday of every month at 1:00 p.m. ET.
A special feature of DOTs is a social component. After a usual talk, you might grab a tea/coffee and chat with other attendees. Why not here too? Join us for some informal discussion after each DOT, as well as throughout the week on our Discord channel.
Join the mailing list to receive information about our upcoming seminars and how to virtually participate. Joining this list is necessary to receive the password for each DOT (usually sent on Monday mornings). You may also wish to subscribe to our Google calendar (also available in ics format).
Born-Again Tree Ensembles: Seeing the Forest for the Trees
The use of machine learning algorithms in finance, medicine, or criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix possible sources of mistakes and biases. Tree ensembles offer a good prediction quality in various domains, but the concurrent use of multiple trees reduces the interpretability of the ensemble. Against this background, we study born-again tree ensembles, i.e., the process of constructing a single decision tree of minimum size that reproduces the exact same behavior as a given tree ensemble in its entire feature space. To find such a tree, we develop a dynamic-programming based algorithm that exploits sophisticated pruning and bounding rules to reduce the number of recursive calls. This algorithm generates optimal born-again trees for many datasets of practical interest, leading to classifiers which are typically simpler and more interpretable without any other form of compromise.
Exact approaches for multiobjective mixed integer nonlinear programming problems
Multiobjective mixed integer optimization refers to mathematical programming problems where more than one objective function needs to be optimized simultaneously and some of the variables are constrained to be integer. We present branch-and-bound algorithms based on the use of properly defined lower bounds. It is possible to show correctness in terms of detecting both the efficient and the nondominated set of multiobjective mixed integer convex problems according to a prescribed precision. Numerical experiments on biobjective and triobjective instances are presented.
Marianna De Santis is associate professor at DIAG, the Department of Computer, Control and Management Engineering of Sapienza, University of Rome, where she got her PhD in Operations Research in 2012 under the supervision of Prof. Stefano Lucidi.
Before coming back to DIAG in 2017, she spent some years as post-doc having the chance to work at IASI (Italian National Research Council), at the Technical University of Dortmund, at the Alpen Adria University of Klagenfurt and at the University of Padova.