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Machine learning based optimization for interval uncertainty propagation
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Machine learning based optimization for interval uncertainty propagation (2022)Machine learning based optimization for i…Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty (2019)Recent Trends in the Modeling…Bayesian Optimization (2018)Bayesian OptimizationA Tutorial on Bayesian Optimization (2018)A Tutorial on Bayesian Optimi…Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)Taking the Human Out of the L…Bayesian optimization in high dimensions via random embeddings (2013)Bayesian optimization in high…Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Information-Theoretic Regret …Convergence rates of efficient global optimization algorithms (2011)Convergence rates of efficien…A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010)A Tutorial on Bayesian Optimi…Info-Gap Decision Theory: Decisions Under Severe Uncertainty (2006)Info-Gap Decision Theory: Dec…Random matrix theory for modeling uncertainties in computational mechanics (2004)Random matrix theory for mode…Computer experiments and global optimization (1997)Computer experiments and glob…Analysis of Uncertain Structural Systems Using Interval Analysis (1997)Analysis of Uncertain Structu…Taguchi's orthogonal arrays are classical designs of experiments (1991)Taguchi's orthogonal arrays a…Polynomial Chaos in Stochastic Finite Elements (1990)Polynomial Chaos in Stochasti…Convex Models of Uncertainty in Applied Mechanics (1990)Convex Models of Uncertainty …A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise (1964)A New Method of Locating the …Design and Analysis of Computer Experiments (1989)Advances in prospect theory: Cumulative representation of uncertainty (1992)The Adaptive Decision Maker (1994)Practical Bayesian Optimization of Machine Learning Algorithms (2012)A Stochastic Approximation Method (1951)High-dimensional Bayesian optimization using low-dimensional feature spaces (2020)Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 (2021)Some aspects of the sequential design of experiments (1952)Finite-time Analysis of the Multiarmed Bandit Problem (2002)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Bandit Algorithms (2020)Antioptimization of structures with large uncertain-but-non-random parameters via interval analysis (1998)Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations (2004)Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles (2011)Scalable Bayesian Optimization Using Deep Neural Networks (2015)Bayesian Optimization for Adaptive Experimental Design: A Review (2020)Uncertainty in Mechanics Problems—Interval–Based Approach (2001)A survey of non-probabilistic uncertainty treatment in finite element analysis (2004)Non-probabilistic finite element analysis for parametric uncertainty treatment in applied mechanics: Recent advances (2010)Bayesian Optimization in a Billion Dimensions via Random Embeddings (2016)Constrained Bayesian Optimization with Noisy Experiments (2018)Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics (2021)A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization (2022)Recent Advances in Bayesian Optimization (2023)Predictions of steel price indices through machine learning for the regional northeast Chinese market (2024)Don't know, can't know: embracing deeper uncertainties when analysing risks (2011)Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info‐Gap Methods (2012)A survey of decision-making approaches for climate change adaptation: Are robust methods the way forward? (2016)
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References

  • 2015Taking the Human Out of the Loop: A Review of Bayesian Optimization6,126 cited
  • 2012Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting2,098 cited
  • 2010A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning2,053 cited
  • 2018A Tutorial on Bayesian Optimization1,422 cited
  • 1964A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise1,084 cited
  • 2018Bayesian Optimization887 cited
  • 1990Convex Models of Uncertainty in Applied Mechanics838 cited
  • 2006Info-Gap Decision Theory: Decisions Under Severe Uncertainty626 cited
  • 2011Convergence rates of efficient global optimization algorithms454 cited
  • 1997Analysis of Uncertain Structural Systems Using Interval Analysis423 cited
  • 1990Polynomial Chaos in Stochastic Finite Elements416 cited
  • 1997Computer experiments and global optimization375 cited
  • 2004Random matrix theory for modeling uncertainties in computational mechanics265 cited
  • 2013Bayesian optimization in high dimensions via random embeddings244 cited
  • 1991Taguchi's orthogonal arrays are classical designs of experiments

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241 cited
  • 2019Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty213 cited
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