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Modern Bayesian Experimental Design
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Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, before discussing some key areas for future development in the field.
Forward citations
Cited by 4 Pith papers
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Enhancing dissipative cat qubit protection by squeezing
A squeezed deformation of dissipative cat qubits, implemented with an extra pump, boosts bit-flip time 160-fold at fixed phase-flip time.
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Active Learning of Model Discrepancy with Bayesian Experimental Design
A hybrid framework alternates Bayesian experimental design for physics parameters with gradient-based calibration of a neural network model-discrepancy term, gated by an ensemble Kalman information-gain indicator.
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Active Task Disambiguation with LLMs
Selecting clarifying questions by estimated information gain over sampled solutions outperforms implicit question generation for LLM task disambiguation.
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Toward Information Theoretic Active Inverse Reinforcement Learning
EIG-based query selection for full-trajectory demonstrations reduces the number of human demonstrations needed to learn a reward in tabular gridworlds compared to random and entropy-based baselines.
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