Pages that link to "Item:Q5271985"
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The following pages link to Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (Q5271985):
Displaying 35 items.
- Constrained, Global Optimization of Unknown Functions with Lipschitz Continuous Gradients (Q5081778) (← links)
- Automated Reinforcement Learning (AutoRL): A Survey and Open Problems (Q5094025) (← links)
- Simple Bayesian Algorithms for Best-Arm Identification (Q5144786) (← links)
- (Q5148929) (← links)
- (Q5148932) (← links)
- Learning Enabled Constrained Black-Box Optimization (Q5153491) (← links)
- Navigating the protein fitness landscape with Gaussian processes (Q5170991) (← links)
- Learning‐based iterative modular adaptive control for nonlinear systems (Q5222720) (← links)
- Learning to Optimize via Posterior Sampling (Q5247618) (← links)
- Sequential Design for Ranking Response Surfaces (Q5269860) (← links)
- Simulation optimization: a review of algorithms and applications (Q5919176) (← links)
- Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics (Q6053801) (← links)
- Online learning‐based model predictive control with Gaussian process models and stability guarantees (Q6060775) (← links)
- Nonlinear learning‐based model predictive control supporting state and input dependent model uncertainty estimates (Q6082344) (← links)
- Efficient hybrid Bayesian optimization algorithm with adaptive expected improvement acquisition function (Q6094430) (← links)
- Multi-fidelity Bayesian optimization to solve the inverse Stefan problem (Q6094642) (← links)
- TREGO: a trust-region framework for efficient global optimization (Q6102171) (← links)
- \textsc{GoSafeOpt}: scalable safe exploration for global optimization of dynamical systems (Q6103669) (← links)
- On the use of Wasserstein distance in the distributional analysis of human decision making under uncertainty (Q6113067) (← links)
- Inverse Bayesian optimization: learning human acquisition functions in an exploration vs exploitation search task (Q6122013) (← links)
- Kernel-based identification with frequency domain side-information (Q6157806) (← links)
- An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models (Q6164276) (← links)
- Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression (Q6184877) (← links)
- Adaptive confidence bound based Bayesian optimization via potentially optimal Lipschitz conditions (Q6193911) (← links)
- A model‐and data‐driven predictive control approach for tracking of stochastic nonlinear systems using Gaussian processes (Q6194773) (← links)
- Moderate deviations inequalities for Gaussian process regression (Q6198967) (← links)
- FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks (Q6333096) (← links)
- Using the knowledge gradient acquisition function in Bayesian optimization when searching for robust solutions (Q6495520) (← links)
- Lower bounds on the noiseless worst-case complexity of efficient global optimization (Q6536837) (← links)
- Efficient constitutive parameter identification through optimisation-based techniques: a comparative analysis and novel composite Bayesian optimisation strategy (Q6557802) (← links)
- Knowledge-based modeling of simulation behavior for Bayesian optimization (Q6584868) (← links)
- Constrained Bayesian Optimization with Lower Confidence Bound (Q6637480) (← links)
- Regularized identification with internal positivity side-information (Q6663097) (← links)
- Tracking control of uncertain nonlinear systems via adaptive Gaussian process prediction and real-time optimisation (Q6666643) (← links)
- A composite Bayesian optimisation framework for material and structural design (Q6669038) (← links)
