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Building Algorithms by Playing Games
Jacob D Abernethy

Fri Dec 07 11:00 AM -- 11:40 AM (PST) @
in Smooth Games Optimization and Machine Learning »

A very popular trick for solving certain types of optimization problems is this: write your objective as the solution of a two-player zero-sum game, endow both players with an appropriate learning algorithm, watch how the opponents compete, and extract an (approximate) solution from the actions/decisions taken by the players throughout the process. This approach is very generic and provides a natural template to produce new and interesting algorithms. I will describe this framework and show how it applies in several scenarios, and describe recent work that draws a connection to the Frank-Wolfe algorithm and Nesterov's Accelerated Gradient Descent.

Author Information

Jacob D Abernethy (University of Michigan)

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