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Jia-Jie Zhu
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2020 – today
- 2024
- [c14]Pavel E. Dvurechensky, Jia-Jie Zhu:
Analysis of Kernel Mirror Prox for Measure Optimization. AISTATS 2024: 2350-2358 - [c13]Egor Gladin, Pavel E. Dvurechenskii, Alexander Mielke, Jia-Jie Zhu:
Interaction-Force Transport Gradient Flows. NeurIPS 2024 - [i23]Ling Liang, Kim-Chuan Toh, Jia-Jie Zhu:
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. CoRR abs/2402.06033 (2024) - [i22]Pavel E. Dvurechensky, Jia-Jie Zhu:
Analysis of Kernel Mirror Prox for Measure Optimization. CoRR abs/2403.00147 (2024) - [i21]Egor Gladin, Pavel E. Dvurechensky, Alexander Mielke, Jia-Jie Zhu:
Interaction-Force Transport Gradient Flows. CoRR abs/2405.17075 (2024) - [i20]Jia-Jie Zhu, Alexander Mielke:
Kernel Approximation of Fisher-Rao Gradient Flows. CoRR abs/2410.20622 (2024) - [i19]Jia-Jie Zhu:
Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective. CoRR abs/2411.00214 (2024) - 2023
- [c12]Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. ICML 2023: 17745-17783 - [i18]Jia-Jie Zhu:
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models. CoRR abs/2304.14057 (2023) - [i17]Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. CoRR abs/2305.10898 (2023) - 2022
- [c11]Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf:
Adversarially Robust Kernel Smoothing. AISTATS 2022: 4972-4994 - [c10]Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Learning Random Feature Dynamics for Uncertainty Quantification. CDC 2022: 4937-4944 - [c9]Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu:
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CDC 2022: 5660-5667 - [c8]Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf:
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML 2022: 11665-11682 - [i16]Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu:
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CoRR abs/2204.11564 (2022) - [i15]Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet
, Bernhard Schölkopf:
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. CoRR abs/2207.04771 (2022) - 2021
- [c7]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. AISTATS 2021: 280-288 - [c6]Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach. L4DC 2021: 1255-1269 - [i14]Jia-Jie Zhu, Yassine Nemmour, Bernhard Schölkopf:
From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing. CoRR abs/2102.08474 (2021) - [i13]Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu, Jan Peters:
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions. CoRR abs/2103.15388 (2021) - [i12]Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics. CoRR abs/2106.13066 (2021) - [i11]Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control. CoRR abs/2110.13588 (2021) - 2020
- [c5]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: 3457-3463 - [c4]Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. L4DC 2020: 915-923 - [i10]Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. CoRR abs/2001.10398 (2020) - [i9]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CoRR abs/2004.00166 (2020) - [i8]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization. CoRR abs/2006.06981 (2020)
2010 – 2019
- 2019
- [c3]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: 170-177 - [c2]Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius:
Control What You Can: Intrinsically Motivated Task-Planning Agent. NeurIPS 2019: 12520-12531 - [i7]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 abs/1906.03684 (2019) - [i6]Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius:
Control What You Can: Intrinsically Motivated Task-Planning Agent. CoRR abs/1906.08190 (2019) - [i5]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 abs/1907.04616 (2019) - [i4]Jia-Jie Zhu, Georg Martius:
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control. CoRR abs/1911.09214 (2019) - [i3]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 abs/1911.11082 (2019) - 2018
- [c1]Dominik Baumann, Sebastian Trimpe
, Jia-Jie Zhu, Georg Martius
:
Deep Reinforcement Learning for Event-Triggered Control. CDC 2018: 943-950 - [i2]Dominik Baumann, Jia-Jie Zhu, Georg Martius, Sebastian Trimpe:
Deep Reinforcement Learning for Event-Triggered Control. CoRR abs/1809.05152 (2018) - 2017
- [i1]Jia-Jie Zhu, José Bento:
Generative Adversarial Active Learning. CoRR abs/1702.07956 (2017)
Coauthor Index

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