Suppose an online platform wants to compare a treatment and control policy (eg, two
different matching algorithms in a ridesharing system, or two different inventory management …

The design of multiple experiments is commonly undertaken via suboptimal strategies, such
as batch (open-loop) design that omits feedback or greedy (myopic) design that does not …

Experimentation is at the core of research in the behavioral and neural sciences, yet
observations can be expensive and time-consuming to acquire (eg, MRI scans, responses …

From clinical development of cancer therapies to investigations into partisan bias, adaptive
sequential designs have become increasingly popular method for causal inference, as they …

Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …

Discriminating among competing statistical models is a pressing issue for many
experimentalists in the field of cognitive science. Resolving this issue begins with designing …

Adaptive designs are effective mechanisms for flexibly allocating experimental resources. In
clinical trials particularly, such designs allow researchers to balance short-and long-term …

Experimentation has been critical and increasingly popular across various domains, such as
clinical trials and online platforms, due to its widely recognized benefits. One of the primary …

We consider computationally tractable methods for the experimental design problem, where
k out of n design points of dimension p are selected so that certain optimality criteria are …

Finite-horizon sequential experimental design (SED) arises naturally in many contexts,
including hyperparameter tuning in machine learning among more traditional settings …