expected improvement

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization JMLR 2025 A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization ICML 2025 Black-box Optimization with Unknown Constraints via Overparameterized Deep Neural Networks UAI 2025 EcoTune: Token-Efficient Multi-Fidelity Hyperparameter Optimization for Large Language Model Inference EMNLP 2025 Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization AAAI 2025 Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization JMLR 2025 A Corrected Expected Improvement Acquisition Function Under Noisy Observations ACML 2023 Constrained Causal Bayesian Optimization ICML 2023 Functional causal Bayesian optimization UAI 2023 Self-Adjusting Weighted Expected Improvement for Bayesian Optimization AutoML 2023 Unexpected Improvements to Expected Improvement for Bayesian Optimization NeurIPS 2023 A General Recipe for Likelihood-free Bayesian Optimization ICML 2022 Expected Improvement for Contextual Bandits NeurIPS 2022 Generalizing Bayesian Optimization with Decision-theoretic Entropies NeurIPS 2022 Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization AISTATS 2022 Asynchronous $ε$-Greedy Bayesian Optimisation UAI 2021 Bayesian Optimization of Function Networks NeurIPS 2021 BORE: Bayesian Optimization by Density-Ratio Estimation ICML 2021 Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs NeurIPS 2021 Distributionally Ambiguous Optimization for Batch Bayesian Optimization JMLR 2020 Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees NeurIPS 2020 Bayesian Optimization of Composite Functions ICML 2019 Practical Two-Step Lookahead Bayesian Optimization NeurIPS 2019 Fast Information-theoretic Bayesian Optimisation ICML 2018 Maximizing acquisition functions for Bayesian optimization NeurIPS 2018