Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

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 …

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 …

In geophysical surveys or experiments, recorded data are used to constrain properties of the
planetary subsurface, oceans, atmosphere or cryosphere. How the experimental data are …

Abstract Successful applications of InfoNCE (Information Noise-Contrastive Estimation) and
its variants have popularized the use of contrastive variational mutual information (MI) …

User models play an important role in interaction design, supporting automation of
interaction design choices. In order to do so, model parameters must be estimated from user …

We formalize the problem of contextual optimization through the lens of Bayesian
experimental design and propose CO-BED—a general, model-agnostic framework for …

Computational models are powerful tools for understanding human cognition and behavior.
They let us express our theories clearly and precisely and offer predictions that can be …

This paper introduces variational design methods that are novel to Geophysics, and
discusses their benefits and limitations in the context of geophysical applications and more …