{"total":30,"rows":[{"id":"2206.03301","title":"Recent Advances in Bayesian Optimization","created":"2022-06-07","cited_by_count":76,"area":null},{"id":"2102.13651","title":"On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning","created":"2021-02-26","cited_by_count":59,"area":null},{"id":"2108.09262","title":"Optimal Order Simple Regret for Gaussian Process Bandits","created":"2021-08-20","cited_by_count":54,"area":null},{"id":"2107.03144","title":"Neural Contextual Bandits without Regret","created":"2021-07-07","cited_by_count":40,"area":null},{"id":"2306.13867","title":"Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems","created":"2023-06-24","cited_by_count":28,"area":null},{"id":"2111.03602","title":"NAS-Bench-x11 and the Power of Learning Curves","created":"2021-11-05","cited_by_count":20,"area":null},{"id":"2306.11715","title":"Multi-Fidelity Active Learning with GFlowNets","created":"2023-06-20","cited_by_count":15,"area":null},{"id":"2506.01393","title":"Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization","created":"2025-06-02","cited_by_count":13,"area":null},{"id":"2406.03033","title":"Optimal Multi-Fidelity Best-Arm Identification","created":"2024-06-05","cited_by_count":9,"area":null},{"id":"2106.00942","title":"JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data","created":"2021-06-02","cited_by_count":8,"area":null},{"id":"2402.09638","title":"Multi-Fidelity Methods for Optimization: A Survey","created":"2024-02-15","cited_by_count":6,"area":null},{"id":"2109.09261","title":"Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization","created":"2021-09-20","cited_by_count":5,"area":null},{"id":"2307.11288","title":"Kernelized Offline Contextual Dueling Bandits","created":"2023-07-21","cited_by_count":4,"area":null},{"id":"2304.06808","title":"Active Cost-aware Labeling of Streaming Data","created":"2023-04-13","cited_by_count":3,"area":null},{"id":"2308.00978","title":"Certified Multi-Fidelity Zeroth-Order Optimization","created":"2023-08-02","cited_by_count":3,"area":null},{"id":"2404.01965","title":"Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks","created":"2024-04-02","cited_by_count":3,"area":null},{"id":"2210.17213","title":"Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors","created":"2022-10-31","cited_by_count":2,"area":null},{"id":"2102.04951","title":"MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2305.04361","title":"Truncating Trajectories in Monte Carlo Reinforcement Learning","created":"2023-05-07","cited_by_count":1,"area":null},{"id":"2501.07964","title":"Derivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process","created":"2025-01-14","cited_by_count":1,"area":null},{"id":"2504.06207","title":"An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2512.15483","title":"Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs","created":"2025-12-17","cited_by_count":1,"area":null},{"id":"2604.06263","title":"Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2604.16239","title":"Adaptive multi-fidelity optimization with fast learning rates","created":"2026-04-17","cited_by_count":1,"area":null},{"id":"2503.00420","title":"A physics-informed Bayesian optimization method for rapid development of electrical machines","created":"2025-03-01","cited_by_count":0,"area":null},{"id":"2605.08558","title":"Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.23208","title":"Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.22238","title":"Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.01378","title":"When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2609.29941","title":"MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}