{"total":13,"rows":[{"id":"2206.10745","title":"Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning","created":"2022-06-21","cited_by_count":44,"area":null},{"id":"2102.06350","title":"Projected Wasserstein gradient descent for high-dimensional Bayesian inference","created":"2021-02-12","cited_by_count":24,"area":null},{"id":"2110.15335","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","created":"2021-10-28","cited_by_count":24,"area":null},{"id":"2310.17844","title":"Adaptive operator learning for infinite-dimensional Bayesian inverse problems","created":"2023-10-27","cited_by_count":11,"area":null},{"id":"2405.09312","title":"Agnostic Active Learning of Single Index Models with Linear Sample Complexity","created":"2024-05-15","cited_by_count":11,"area":null},{"id":"2106.07908","title":"Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation","created":"2021-06-15","cited_by_count":2,"area":null},{"id":"2406.13425","title":"Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis","created":"2024-06-19","cited_by_count":2,"area":null},{"id":"2305.03855","title":"Robust A-Optimal Experimental Design for Bayesian Inverse Problems","created":"2023-05-05","cited_by_count":1,"area":null},{"id":"2406.05830","title":"Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement","created":"2024-06-09","cited_by_count":1,"area":null},{"id":"2101.05958","title":"Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments","created":"2021-01-15","cited_by_count":0,"area":null},{"id":"2306.17615","title":"Scalable method for Bayesian experimental design without integrating over posterior distribution","created":"2023-06-30","cited_by_count":0,"area":null},{"id":"2403.03233","title":"From Displacements to Distributions: A Machine-Learning Enabled Framework for Quantifying Uncertainties in Parameters of Computational Models","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2604.21849","title":"Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions","created":"2026-04-23","cited_by_count":0,"area":null}],"next_cursor":null}