A certificate-based regret analysis framework for guided-diffusion black-box optimization is introduced, with mass lift as the central quantity explaining convergence from pretrained generators.
Cambridge University Press
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
An LLM-based evolutionary search discovers novel kernels for high-dimensional Bayesian optimization, achieving an average rank of 1.2 out of 17 on five benchmarks via two-stage proposal and LOO-CRPS selection.
Proposes a vector-valued RKHS framework for Bayesian optimization with structured measurements, deriving concentration bounds and UCB-based regret guarantees that recover sublinear rates.
OSCBO adaptively balances Gaussian process sharpness and calibration in Bayesian optimization by casting hyperparameter selection as constrained online learning, while preserving sublinear regret bounds.
Generate-Select-Refine is an open-ended Bayesian optimization method that generates tasks and concentrates evaluations on the best one with only logarithmic regret overhead relative to standard single-task optimization.
FICBO pretrains a feedback-aware transformer with a structured prior on feedback distortion to adaptively exploit or ignore unreliable auxiliary signals during in-context black-box optimization.
An AI interoperability framework between FINALES and Kadi4Mat uses batched Bayesian optimization to explore trade-offs between shorter formation time and higher end-of-life performance in sodium-ion coin cells.
citing papers explorer
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Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs
A certificate-based regret analysis framework for guided-diffusion black-box optimization is introduced, with mass lift as the central quantity explaining convergence from pretrained generators.
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Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
An LLM-based evolutionary search discovers novel kernels for high-dimensional Bayesian optimization, achieving an average rank of 1.2 out of 17 on five benchmarks via two-stage proposal and LOO-CRPS selection.
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Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
Proposes a vector-valued RKHS framework for Bayesian optimization with structured measurements, deriving concentration bounds and UCB-based regret guarantees that recover sublinear rates.
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Online Sharp-Calibrated Bayesian Optimization
OSCBO adaptively balances Gaussian process sharpness and calibration in Bayesian optimization by casting hyperparameter selection as constrained online learning, while preserving sublinear regret bounds.
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Open-Ended Task Discovery via Bayesian Optimization
Generate-Select-Refine is an open-ended Bayesian optimization method that generates tasks and concentrates evaluations on the best one with only logarithmic regret overhead relative to standard single-task optimization.
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In-Context Black-Box Optimization with Unreliable Feedback
FICBO pretrains a feedback-aware transformer with a structured prior on feedback distortion to adaptively exploit or ignore unreliable auxiliary signals during in-context black-box optimization.
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Accelerating battery research with an AI interface between FINALES and Kadi4Mat
An AI interoperability framework between FINALES and Kadi4Mat uses batched Bayesian optimization to explore trade-offs between shorter formation time and higher end-of-life performance in sodium-ion coin cells.