Jia-Jie Zhu - RankMe

41 papers A* 4A 3Journal 27Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics. CoRR Sarah Katz, Francesco Romor, Jia-Jie Zhu, Alfonso Caiazzo
2025	J	`jnl`	An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. J. Optim. Theory Appl. Ling Liang, Zusen Xu, Kim-Chuan Toh, Jia-Jie Zhu
2025	J	`jnl`	Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals. CoRR Alexander Mielke, Jia-Jie Zhu
2025	J	`jnl`	Improved Stochastic Optimization of LogSumExp. CoRR Egor Gladin, Alexey Kroshnin, Jia-Jie Zhu, Pavel E. Dvurechensky
2024	J	`jnl`	An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. CoRR Ling Liang, Kim-Chuan Toh, Jia-Jie Zhu
2024	A	`conf`	Analysis of Kernel Mirror Prox for Measure Optimization. AISTATS Pavel E. Dvurechensky, Jia-Jie Zhu
2024	J	`jnl`	Analysis of Kernel Mirror Prox for Measure Optimization. CoRR Pavel E. Dvurechensky, Jia-Jie Zhu
2024	J	`jnl`	Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective. CoRR Jia-Jie Zhu
2024	A*	`conf`	Interaction-Force Transport Gradient Flows. NeurIPS Egor Gladin, Pavel E. Dvurechenskii, Alexander Mielke, Jia-Jie Zhu
2024	J	`jnl`	Interaction-Force Transport Gradient Flows. CoRR Egor Gladin, Pavel E. Dvurechensky, Alexander Mielke, Jia-Jie Zhu
2024	J	`jnl`	Kernel Approximation of Fisher-Rao Gradient Flows. CoRR Jia-Jie Zhu, Alexander Mielke
2023	A*	`conf`	Estimation Beyond Data Reweighting: Kernel Method of Moments. ICML Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
2023	J	`jnl`	Estimation Beyond Data Reweighting: Kernel Method of Moments. CoRR Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
2023	J	`jnl`	Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models. CoRR Jia-Jie Zhu
2022	A	`conf`	Adversarially Robust Kernel Smoothing. AISTATS Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf
2022	A*	`conf`	Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
2022	J	`jnl`	Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. CoRR Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
2022	—	`conf`	Learning Random Feature Dynamics for Uncertainty Quantification. CDC Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
2022	—	`conf`	Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CDC Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu
2022	J	`jnl`	Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CoRR Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu
2021	—	`conf`	Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach. L4DC Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
2021	J	`jnl`	Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control. CoRR Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
2021	J	`jnl`	Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions. CoRR Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu, Jan Peters
2021	J	`jnl`	From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing. CoRR Jia-Jie Zhu, Yassine Nemmour, Bernhard Schölkopf
2021	A	`conf`	Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. AISTATS Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
2021	J	`jnl`	Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics. CoRR Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
2020	—	`conf`	A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. L4DC Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl
2020	J	`jnl`	A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. CoRR Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl
2020	J	`jnl`	Kernel Distributionally Robust Optimization. CoRR Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
2020	—	`conf`	Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CDC Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
2020	J	`jnl`	Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CoRR Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf
2019	J	`jnl`	A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. CoRR Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf
2019	A*	`conf`	Control What You Can: Intrinsically Motivated Task-Planning Agent. NeurIPS Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius
2019	J	`jnl`	Control What You Can: Intrinsically Motivated Task-Planning Agent. CoRR Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius
2019	J	`jnl`	Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control. CoRR Jia-Jie Zhu, Georg Martius
2019	—	`conf`	Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning. Humanoids Mohammad Hasan Yeganegi, Majid Khadiv, S. Ali A. Moosavian, Jia-Jie Zhu, Andrea Del Prete, Ludovic Righetti
2019	J	`jnl`	Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning. CoRR Mohammad Hasan Yeganegi, Majid Khadiv, S. Ali A. Moosavian, Jia-Jie Zhu, Andrea Del Prete, Ludovic Righetti
2019	J	`jnl`	Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning. CoRR Majid Khadiv, Mohammad Hasan Yeganegi, S. Ali A. Moosavian, Jia-Jie Zhu, Ludovic Righetti
2018	—	`conf`	Deep Reinforcement Learning for Event-Triggered Control. CDC Dominik Baumann, Sebastian Trimpe, Jia-Jie Zhu, Georg Martius
2018	J	`jnl`	Deep Reinforcement Learning for Event-Triggered Control. CoRR Dominik Baumann, Jia-Jie Zhu, Georg Martius, Sebastian Trimpe
2017	J	`jnl`	Generative Adversarial Active Learning. CoRR Jia-Jie Zhu, José Bento