Introduces BOBa, a multi-armed bandit method for scalable surrogate optimization that adaptively allocates inference and evaluations to promising partitions of ultra-large chemical libraries.
Laplace Redux – Effortless Bayesian Deep Learning
4 Pith papers cite this work, alongside 27 external citations. Polarity classification is still indexing.
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Pith papers citing it
27
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UNVERDICTED 4representative citing papers
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
A perturbation-based conformal prediction wrapper on Fourier Neural Operators yields narrower uncertainty bands than prior methods for 2D incompressible Navier-Stokes while preserving coverage in data-scarce regimes.
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
citing papers explorer
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Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space
Introduces BOBa, a multi-armed bandit method for scalable surrogate optimization that adaptively allocates inference and evaluations to promising partitions of ultra-large chemical libraries.
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Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
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Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime
A perturbation-based conformal prediction wrapper on Fourier Neural Operators yields narrower uncertainty bands than prior methods for 2D incompressible Navier-Stokes while preserving coverage in data-scarce regimes.
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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.