1 citations · 4 across the 10 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…
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee
Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf +1
This paper is motivated by addressing open questions in distributionally robust chance-constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically, the…
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control
Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
We provide a functional view of distributional robustness motivated by robust statistics and functional analysis. This results in two practical computational approaches for approxi…
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics
Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf +1
Random features is a powerful universal function approximator that inherits the theoretical rigor of kernel methods and can scale up to modern learning tasks. This paper views unce…
Adversarially Robust Kernel Smoothing
Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour +1
We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally r…