Workshop on Automated Algorithm Design


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Aug 7 — Aug 9, 2019
Toyota Technological Institute

This workshop focuses on new machine learning techniques for automatically designing algorithms. Algorithms are central to modern computing, and they have lots of applications in our life. Yet, writing correct, efficient algorithms is a time-consuming and difficult task. It also often requires intuition and expertise to tailor algorithmic choices to specific instances that arise in particular applications. However, there have been a number of recent advancements that have allowed algorithms to be selected or designed from specific algorithmic families automatically, often leading to either state-of-the-art empirical performance or provable performance guarantees on observed instance distributions. In this workshop, we take a broad view of the problem and seek to bring together researchers with different viewpoints and approaches to the general challenge.

3

Days

20

Talks

1

Panel

Awesomeness

Logistics

Organizers

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Nina Balcan
Carnegie Mellon University

Nature

Bistra Dilkina
University of Southern California

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Carl Kingsford
Carnegie Mellon University

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Paul Medvedev
Penn State University

Speakers

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Brendan Lucier
Microsoft Research

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Piotr Indyk
Massachusetts Institute of Technology

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Kevin Leyton-Brown
University of British Columbia

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Maxime Gasse
École Polytechnique de Montréal

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Vincent Conitzer
Duke University

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Devon Graham
University of British Columbia

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Charles Sutton
Google

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Elias Khalil
University of Toronto

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Csaba Szepesvari
University of Alberta

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Dan DeBlasio
Carnegie Mellon University

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John Kececioglu
University of Arizona

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Tim Roughgarden
Columbia University

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Alexei Novikov
Penn State University

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Tuomas Sandholm
Carnegie Mellon University

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Ellen Vitercik
Carnegie Mellon University

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Yisong Yue
California Institute of Technology