POLAR uses pretrained predictive foundation models as fixed belief-state encoders and trains only a lightweight policy head on top for amortised Bayesian experimental design, optimisation, and active learning.
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Pith papers citing it
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2026 2verdicts
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Combines offline amortized pre-training with online scenario-tree planning to optimize constrained Bayesian experimental designs, producing more informative sequences than prior methods.
citing papers explorer
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Efficient Adaptive Data Acquisition via Pretrained Belief Representations
POLAR uses pretrained predictive foundation models as fixed belief-state encoders and trains only a lightweight policy head on top for amortised Bayesian experimental design, optimisation, and active learning.
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Constrained Bayesian Experimental Design via Online Planning
Combines offline amortized pre-training with online scenario-tree planning to optimize constrained Bayesian experimental designs, producing more informative sequences than prior methods.