ABSTRACT: We consider sequential prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown constraint set. We quantify relaxations of [...]

ABSTRACT: Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical understandings lag behind. In [...]

ABSTRACT: The most successful method for derivative-free optimization developed by the optimization community in the last 2 decades was proposed by Powell. It creates quadratic [...]

exploretech.la is an annual event hosted by UCLA students that aims to inspire high school students from underserved communities in the Greater Los Angeles Area [...]

ABSTRACT: Recent work has shown that tools from dynamical systems can be used to analyze accelerated optimization algorithms. For example, it has been shown that [...]

ABSTRACT: The financial services industry needs fairness and explainability in artificial intelligence and machine learning, arising from considerations of transparency, ethics, regulatory compliance, and risk [...]