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

Abstract We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of
adaptive Bayesian experimental design that allows experiments to be run in real-time …

We present a mathematical framework and computational methods for optimally designing a
finite sequence of experiments. This sequential optimal experimental design (sOED) …

Abstract We introduce implicit Deep Adaptive Design (iDAD), a new method for performing
adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian …

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 …

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

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 field of optimal experimental design uses mathematical techniques to determine
experiments that are maximally informative from a given experimental setup. Here we apply …

We introduce a gradient-based approach for the problem of Bayesian optimal experimental
design to learn causal models in a batch setting—a critical component for causal discovery …

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 …