Algorithms and Theory

In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19’s spread. Unfortunately, we cannot directly observe the prevalence or growth rate of COVID-19; these must be inferred using some kind of model. We propose a hierarchical Bayesian extension to the classic susceptible-...

We study the structure of regret-minimizing policies in the {\em many-armed} Bayesian multi-armed bandit problem: in particular, with $k$ the number of arms and $T$ the time horizon, we consider the case where $k \geq \sqrt{T}$. We first show that {\em subsampling} is a critical step for designing optimal policies. In particular, the standard UCB algorithm leads to sub-optimal regret bounds in...

Privacy protection is paramount in designing and running health studies. However, most health research to date uses data stored in a centralized database, where analysis and model fitting is done with full access to the sensitive underlying data. Recent advances in federated learning enable building complex machine-learned models that are trained in a purely distributed fashion such that...

We introduce a minmax problem extending optimal transport (OT) when multiple costs are considered. We consider a linear optimization problem which allows to choose locally among $N\geq 1$ rescaled cost functions in order to minimize the cost of transport, yet achieve global robustness by maximizing against those scalings subject to simplicial constraint. When $N=1$, we recover the classical OT...