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Relevant Projects

Photo of Nir Ailon
Associate Professor
Dimensionality reduction

In this setting we study how to reduce the dimensionality of data for learning and for optimization, avoiding the “curse of dimensionality”.

Ranking and preference learning

In this setting we study how to model people’s preferences over a set of choices, and how to optimize and learn given user preferences in a variety of applications.

Online and bandit optimization

In this project we study how to make decisions in an unknown environment in an online setting.

Large matrix approximation for acceleration of deep networks

In this work we apply matrix approximation theory to reduce the cost of training and deploying of dense layers in deep networks.