Abstract: Many existing literature on bandits and reinforcement learning assume a linear reward/value function, but what happens if the reward is non-linear? Two curious phenomena arise for non-linear bandits: first, in addition to the "learning phase" with a standard \Theta(\sqrt(T)) regret, there is an "initialization phase" with a fixed cost determined by the reward function; second, achieving the smallest cost of the initialization phase requires new learning algorithms other than traditional ones such as UCB. For a special family of…

ABSTRACT Recent advances in data collection have made sports an attractive testing ground for new analyses and algorithms, and a fascinating controlled microcosm in which to explore social interactions. In this talk I will describe two studies in this arena: one related to public health and the pandemic and one related to decision-making in basketball.  In the first, I will discuss what can be learned from the natural experiments that were (fortuitously) run in America football stadiums. During the 2020…