Machine Learning helps Discrete Optimization

When:
Friday, November 16, 2018, 11:00 am - 12:00 pm PSTiCal
Where:
10th floor conference room (1016)
This event is open to the public.
Type:
AI Seminar
Speaker:
Bistra Dilkina, USC
Video Recording:
https://bluejeans.com/s/73i6n/
Description:

This talk focuses on a novel fruitful synergy between machine learning
and optimization --- in particular, how ML techniques can improve the
design of algorithms for Discrete Optimization, both complete
algorithms such as branch and bound as well as incomplete ones such as
heuristic greedy search. Branch and Bound solvers for Mixed Integer
Programs (MIP) such as CPLEX, Gurobi and SCIP are used daily across
different domains and industries to find solutions with optimality
guarantees for NP-hard combinatorial problems. Leveraging the plethora
of rich and useful data generated during the solving process, we
illustrate the potential for ML in MIP on two crucial tasks in branch
and bound: branching variable selection and primal heuristic
selection. Empirical results show that our novel approaches can
significantly improve the performance of a solver on both
heterogeneous benchmark instances as well as homogeneous families of
instances. In the second part of the talk, we show how to leverage a
unique combination of reinforcement learning and graph embedding to
infer very effective data-driven greedy strategies for solving
well-studied combinatorial optimization problems on graphs such as
Minimum Vertex Cover, Max Cut and Traveling Salesman.

Bio:
Bistra Dilkina is a Gabilan Assistant Professor of Computer Science at
the University of Southern California and an Associate Director of the USC Center for AI in Society (CAIS), since January 2018. Before that, Dilkina was as an Assistant Professor in the College of
Computing at the Georgia Institute of Technology and a co-director of
the Data Science for Social Good Atlanta summer program. She received
her PhD from Cornell University, and was a Post-Doctoral Associate at
the Institute for Computational Sustainability. Her work spans
discrete optimization, machine learning, network design, and
stochastic optimization. Dilkina's research focuses on advancing the
state of the art for solving real-world large-scale combinatorial
optimization problems, particularly ones that arise in sustainability
areas such as biodiversity conservation planning and urban planning.
Dilkina is one of the junior faculty leaders in the young field of
Computational Sustainability, and has co-organized workshops,
tutorials, special tracks at AAAI and IJCAI, as well as a doctoral consortium on
Computational Sustainability.

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