Spring 2022

Adversarial Approaches in Machine Learning

Tuesday, Feb 22, 2022 to Friday, Feb 25, 2022 

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Constantinos Daskalakis (Massachusetts Institute of Technology; chair), Niao He (ETH Zürich), Michael Jordan (UC Berkeley), Asu Özdağlar (Massachusetts Institute of Technology), Georgios Piliouras (Singapore University of Technology and Design), Vasilis Syrgkanis (Microsoft Research)

Many recent machine learning approaches have moved from an optimization perspective to an "equilibration" perspective, where a good model is framed as the equilibrium of a game, as opposed to the minimizer of an objective function. Examples include generative adversarial networks, adversarial robustness, and fairness in machine learning. While recent years have seen great progress in nonconvex optimization, with celebrated methods such stochastic gradient descent, Adagrad, and Adam driving much of the progress in deep learning, our ability to solve min-max optimization problems and finding equilibria in smooth games remains rather poor (especially when payoffs are nonconvex/concave or noisy). This workshop will bring together practitioners from these fields to discuss the practical challenges, as well as researchers working on the foundations of stochastic optimization and algorithmic game theory.

Registration is required to attend this boot camp. Space may be limited, and you are advised to register early. The link to the registration form will appear on this page approximately 10 weeks before the boot camp. Please await confirmation of your acceptance before booking your travel.

Further details about this workshop will be posted in due course. To contact the organizers about this workshop, please complete this form.

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