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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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3 results for “Infill-Sampling”

Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Functionmath.OC

This paper develops a Bayesian optimal design of experiments for estimating the statistical expectation of a black-box function.

problem Estimating the statistical expectation of a black-box function in complex systems.

method Sequentially querying the black-box function at specific designs selected by an infill-sampling criterion, maximizing expected information gain.

result Derivation of a semi-analytic mathematical formula for expected information gain about the statistical expectation of a physical response.

Bayesian Optimal Design Black-Box Function Statistical Expectation Infill-Sampling Information Gain

2018-07-26abs ↗pdf ↗

Global optimization of expensive black-box models based on asynchronous hybrid-criterion with interval reductionstat.ML

Develops an SSBO algorithm for global optimization of expensive models.

problem Global optimization of expensive black-box models.

method Asynchronous hybrid-criterion with interval reduction.

result Improves global search ability and local search efficiency.

optimization black-box models sequential surrogate-based genetic algorithm sequential quadratic programming

2018-11-29abs ↗pdf ↗

A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Samplingstat.ML

Proposes a method to improve surrogate modeling and design optimization using latent variables.

problem Improving efficiency in multi-fidelity adaptive sampling without hierarchical assumptions.

method A framework using a latent variable Gaussian process to capture correlations between different fidelity models and optimize adaptive sampling.

result Demonstrates superior performance in convergence rate and robustness compared to existing methods.

latent variable multi-fidelity adaptive sampling Gaussian process Bayesian optimization

2023-10-05abs ↗pdf ↗