{"total":17,"rows":[{"id":"2104.10201","title":"Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020","created":"2021-04-20","cited_by_count":148,"area":null},{"id":"2106.00421","title":"OpenBox: A Generalized Black-box Optimization Service","created":"2021-06-01","cited_by_count":46,"area":null},{"id":"2206.07438","title":"Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview","created":"2022-06-15","cited_by_count":37,"area":null},{"id":"2110.13293","title":"Emulation of physical processes with Emukit","created":"2021-10-25","cited_by_count":24,"area":null},{"id":"2110.15172","title":"Conditioning Sparse Variational Gaussian Processes for Online Decision-making","created":"2021-10-28","cited_by_count":23,"area":null},{"id":"2306.09803","title":"Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization","created":"2023-06-16","cited_by_count":16,"area":null},{"id":"2212.11142","title":"BaCO: A Fast and Portable Bayesian Compiler Optimization Framework","created":"2022-12-01","cited_by_count":11,"area":null},{"id":"2302.08436","title":"Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow","created":"2023-02-16","cited_by_count":11,"area":null},{"id":"2103.16649","title":"Revisiting Bayesian Optimization in the light of the COCO benchmark","created":"2021-03-30","cited_by_count":10,"area":null},{"id":"2304.13339","title":"OpenBox: A Python Toolkit for Generalized Black-box Optimization","created":"2023-04-26","cited_by_count":9,"area":null},{"id":"2204.05411","title":"$\\{\\text{PF}\\}^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization","created":"2022-04-11","cited_by_count":5,"area":null},{"id":"2302.08298","title":"Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization","created":"2023-02-16","cited_by_count":5,"area":null},{"id":"2205.03261","title":"Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design","created":"2022-05-06","cited_by_count":4,"area":null},{"id":"2306.15576","title":"PyBADS: Fast and robust black-box optimization in Python","created":"2023-06-27","cited_by_count":3,"area":null},{"id":"2306.16913","title":"AutoML in Heavily Constrained Applications","created":"2023-06-29","cited_by_count":3,"area":null},{"id":"2507.02365","title":"Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representations","created":"2025-07-03","cited_by_count":1,"area":null},{"id":"2509.17405","title":"Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization","created":"2025-09-22","cited_by_count":0,"area":null}],"next_cursor":null}