Jia-Jie Zhu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 34

Top Topics

Robust Optimization

Chance Constrained

Efficient Learning

Kernel Function

Top Venues

Publications

Ling Liang, Kim-Chuan Toh, Jia-Jie Zhu
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. CoRR (2024)
Pavel E. Dvurechensky, Jia-Jie Zhu
Analysis of Kernel Mirror Prox for Measure Optimization. AISTATS (2024)
Egor Gladin, Pavel E. Dvurechensky, Alexander Mielke, Jia-Jie Zhu
Interaction-Force Transport Gradient Flows. CoRR (2024)
Pavel E. Dvurechensky, Jia-Jie Zhu
Analysis of Kernel Mirror Prox for Measure Optimization. CoRR (2024)
Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Estimation Beyond Data Reweighting: Kernel Method of Moments. CoRR (2023)
Jia-Jie Zhu
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models. CoRR (2023)
Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Estimation Beyond Data Reweighting: Kernel Method of Moments. ICML (2023)
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. CoRR (2022)
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML (2022)
Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CoRR (2022)
Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CDC (2022)
Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf
Adversarially Robust Kernel Smoothing. AISTATS (2022)
Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Learning Random Feature Dynamics for Uncertainty Quantification. CDC (2022)
Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics. CoRR (2021)
Jia-Jie Zhu, Yassine Nemmour, Bernhard Schölkopf
From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing. CoRR (2021)
Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu, Jan Peters
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions. CoRR (2021)
Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control. CoRR (2021)
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. AISTATS (2021)
Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach. L4DC (2021)
Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. CoRR (2020)
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
Kernel Distributionally Robust Optimization. CoRR (2020)
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CoRR (2020)
Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. L4DC (2020)
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CDC (2020)
Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. CoRR (2019)
Majid Khadiv, Mohammad Hasan Yeganegi, S. Ali A. Moosavian, Jia-Jie Zhu, Ludovic Righetti
Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning. CoRR (2019)
Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius
Control What You Can: Intrinsically Motivated Task-Planning Agent. NeurIPS (2019)
Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius
Control What You Can: Intrinsically Motivated Task-Planning Agent. CoRR (2019)
Mohammad Hasan Yeganegi, Majid Khadiv, S. Ali A. Moosavian, Jia-Jie Zhu, Andrea Del Prete, Ludovic Righetti
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning. Humanoids (2019)
Jia-Jie Zhu, Georg Martius
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control. CoRR (2019)
Mohammad Hasan Yeganegi, Majid Khadiv, S. Ali A. Moosavian, Jia-Jie Zhu, Andrea Del Prete, Ludovic Righetti
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning. CoRR (2019)
Dominik Baumann, Sebastian Trimpe, Jia-Jie Zhu, Georg Martius
Deep Reinforcement Learning for Event-Triggered Control. CDC (2018)
Dominik Baumann, Jia-Jie Zhu, Georg Martius, Sebastian Trimpe
Deep Reinforcement Learning for Event-Triggered Control. CoRR (2018)
Jia-Jie Zhu, José Bento
Generative Adversarial Active Learning. CoRR (2017)