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.
None to optima in few shots: Bayesian optimization with mdp priors
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Complementing tabular foundation model pretraining with LSBO-specific synthetic tasks and a regularizer yields strong performance on held-out molecular optimization benchmarks.
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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In-Context Learning for Latent Space Bayesian Optimization
Complementing tabular foundation model pretraining with LSBO-specific synthetic tasks and a regularizer yields strong performance on held-out molecular optimization benchmarks.