Jia-Jie Zhu

According to our database1, Jia-Jie Zhu authored at least 26 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Kernel Approximation of Fisher-Rao Gradient Flows.
CoRR, 2024

Interaction-Force Transport Gradient Flows.
CoRR, 2024

An Inexact Halpern Iteration with Application to Distributionally Robust Optimization.
CoRR, 2024

Analysis of Kernel Mirror Prox for Measure Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models.
CoRR, 2023

Estimation Beyond Data Reweighting: Kernel Method of Moments.
Proceedings of the International Conference on Machine Learning, 2023

2022
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions.
Proceedings of the International Conference on Machine Learning, 2022

Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Learning Random Feature Dynamics for Uncertainty Quantification.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Adversarially Robust Kernel Smoothing.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control.
CoRR, 2021

Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics.
CoRR, 2021

Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions.
CoRR, 2021

From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing.
CoRR, 2021

Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Kernel Distributionally Robust Optimization.
CoRR, 2020

A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming.
CoRR, 2019

Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control.
CoRR, 2019

Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning.
CoRR, 2019

Control What You Can: Intrinsically Motivated Task-Planning Agent.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning.
Proceedings of the 19th IEEE-RAS International Conference on Humanoid Robots, 2019

2018
Deep Reinforcement Learning for Event-Triggered Control.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017
Generative Adversarial Active Learning.
CoRR, 2017


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