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Mar 30, 2026LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamicsApr 29, 2025Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flowJan 28, 2025Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy FunctionalsOct 31, 2024Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow PerspectiveOct 27, 2024Kernel Approximation of Fisher-Rao Gradient FlowsMay 27, 2024Interaction-Force Transport Gradient FlowsFeb 29, 2024Analysis of Kernel Mirror Prox for Measure OptimizationMay 18, 2023Estimation Beyond Data Reweighting: Kernel Method of MomentsApr 27, 2023Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics ModelsJul 11, 2022Functional Generalized Empirical Likelihood Estimation for Conditional Moment RestrictionsApr 25, 2022Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample GuaranteeMar 29, 2021Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-RegionsJun 12, 2020Kernel Distributionally Robust OptimizationMar 31, 2020Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment ProblemJan 28, 2020A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and ControlNov 25, 2019A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic ProgrammingNov 20, 2019Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive ControlJun 19, 2019Control What You Can: Intrinsically Motivated Task-Planning AgentFeb 25, 2017Generative Adversarial Active LearningApr 16, 2014Projection Algorithms for Non-Convex Minimization with Application to Sparse Principal Component Analysis