1 citations · 3 across the 9 of their papers we have counts for
10 papers
LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
Sarah Katz, Francesco Romor, Jia-Jie Zhu +1
We introduce a novel conditional stochastic interpolant framework for generative modeling of three-dimensional shapes. The method builds on a recent LDDMM-based registration approa…
Gradient Flow Sampler-based Distributionally Robust Optimization
Zusen Xu, Jia-Jie Zhu
We propose a mathematically principled PDE gradient flow framework for distributionally robust optimization (DRO). Exploiting the recent advances in the intersection of Markov Chai…
Pricing American options under rough volatility using deep-signatures and signature-kernels
Christian Bayer, Luca Pelizzari, Jia-Jie Zhu
We extend the signature-based primal and dual solutions to the optimal stopping problem recently introduced in [Bayer et al.: Primal and dual optimal stopping with signatures, to a…
Estimation Beyond Data Reweighting: Kernel Method of Moments
Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf +1
Moment restrictions and their conditional counterparts emerge in many areas of machine learning and statistics ranging from causal inference to reinforcement learning. Estimators f…
Nonlinear Wasserstein Distributionally Robust Optimal Control
Zhengang Zhong, Jia-Jie Zhu
This paper presents a novel approach to addressing the distributionally robust nonlinear model predictive control (DRNMPC) problem. Current literature primarily focuses on the stat…
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet +1
Important problems in causal inference, economics, and, more generally, robust machine learning can be expressed as conditional moment restrictions, but estimation becomes challeng…