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Learning Meets Combinatorial Algorithms
Marin Vlastelica · Jialin Song · Aaron Ferber · Brandon Amos · Georg Martius · Bistra Dilkina · Yisong Yue

Sat Dec 12 03:00 AM -- 04:00 PM (PST) @ None
Event URL: https://sites.google.com/view/lmca2020/home »

We propose to organize a workshop on machine learning and combinatorial algorithms. The combination of methods from machine learning and classical AI is an emerging trend. Many researchers have argued that “future AI” methods somehow need to incorporate discrete structures and symbolic/algorithmic reasoning. Additionally, learning-augmented optimization algorithms can impact the broad range of difficult but impactful optimization settings. Coupled learning and combinatorial algorithms have the ability to impact real-world settings such as hardware & software architectural design, self-driving cars, ridesharing, organ matching, supply chain management, theorem proving, and program synthesis among many others. We aim to present diverse perspectives on the integration of machine learning and combinatorial algorithms.

This workshop aims to bring together academic and industrial researchers in order to describe recent advances and build lasting communication channels for the discussion of future research directions pertaining the integration of machine learning and combinatorial algorithms. The workshop will connect researchers with various relevant backgrounds, such as those working on hybrid methods, have particular expertise in combinatorial algorithms, work on problems whose solution likely requires new approaches, as well as everyone interested in learning something about this emerging field of research. We aim to highlight open problems in bridging the gap between machine learning and combinatorial optimization in order to facilitate new research directions.
The workshop will foster the collaboration between the communities by curating a list of problems and challenges to promote the research in the field.

Our technical topics of interest include (but are not limited to):
- Hybrid architectures with combinatorial building blocks
- Attacking hard combinatorial problems with learning
- Neural architectures mimicking combinatorial algorithms

Further information about speakers, paper submissions and schedule are available at the workshop website: https://sites.google.com/view/lmca2020/home .

Author Information

Marin Vlastelica (Max Planck Institute for Intelligent Systems)

Marin Vlastelica is a PhD student in the Autonomous Learning group at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research interests involve the interplay between combinatorial algorithms and ML, reinforcement learning, and causality with the goal of improving sample efficiency in sequential decision making processes.

Jialin Song (Caltech)
Aaron Ferber (University of Southern California)
Brandon Amos (Facebook AI)
Georg Martius (MPI for Intelligent Systems)
Bistra Dilkina (University of Southern California)
Yisong Yue (Caltech)

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