Jia-Jie Zhu
Cited by
Cited by
Generative adversarial active learning
JJ Zhu, J Bento
arXiv preprint arXiv:1702.07956, 2017
Deep reinforcement learning for event-triggered control
D Baumann, JJ Zhu, G Martius, S Trimpe
2018 IEEE Conference on Decision and Control (CDC), 943-950, 2018
Kernel distributionally robust optimization: Generalized duality theorem and stochastic approximation
JJ Zhu, W Jitkrittum, M Diehl, B Schölkopf
International Conference on Artificial Intelligence and Statistics, 280-288, 2021
Control What You Can: Intrinsically Motivated Task-Planning Agent
S Blaes, MV Pogančić, JJ Zhu, G Martius
Advances in Neural Information Processing Systems, 2019, 2019
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning*
MH Yeganegi, M Khadiv, SAA Moosavian, JJ Zhu, A Del Prete, L Righetti
2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids …, 2019
A metric for sets of trajectories that is practical and mathematically consistent
J Bento, JJ Zhu
arXiv preprint arXiv:1601.03094, 2016
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Hybrid Model Predictive Control
JJ Zhu, G Martius
IFAC-PapersOnLine 53 (2), 5239-5245, 2020
Projection algorithms for nonconvex minimization with application to sparse principal component analysis
WW Hager, DT Phan, JJ Zhu
Journal of Global Optimization 65 (4), 657-676, 2016
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
JJ Zhu, W Jitkrittum, M Diehl, B Schölkopf
2020 59th IEEE Conference on Decision and Control (CDC), 3457-3463, 2020
A new distribution-free concept for representing, comparing, and propagating uncertainty in dynamical systems with kernel probabilistic programming
JJ Zhu, K Muandet, M Diehl, B Schölkopf
IFAC-PapersOnLine 53 (2), 7240-7247, 2020
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
JJ Zhu, M Diehl, B Schölkopf
Proceedings of the 2nd Conference on Learning for Dynamics and Control …, 2020
Generative adversarial active learning. arXiv 2017
J Zhu, J Bento
arXiv preprint arXiv:1702.07956, 0
Maximum mean discrepancy distributionally robust nonlinear chance-constrained optimization with finite-sample guarantee
Y Nemmour, H Kremer, B Schölkopf, JJ Zhu
2022 IEEE 61st Conference on Decision and Control (CDC), 5660-5667, 2022
Functional generalized empirical likelihood estimation for conditional moment restrictions
H Kremer, JJ Zhu, K Muandet, B Schölkopf
International Conference on Machine Learning, 11665-11682, 2022
Adversarially Robust Kernel Smoothing
JJ Zhu, C Kouridi, Y Nemmour, B Schölkopf
arXiv preprint arXiv:2102.08474, 2021
A decentralized multi-block ADMM for demand-side primary frequency control using local frequency measurements
J Brooks, W Hager, J Zhu
arXiv preprint arXiv:1509.08206, 2015
Generative adversarial active learning. arXiv
J Zhu, J Bento
arXiv preprint arXiv:1702.07956, 2017
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control
Y Nemmour, B Schölkopf, JJ Zhu
arXiv preprint arXiv:2110.13588, 2021
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions
H Abdulsamad, T Dorau, B Belousov, JJ Zhu, J Peters
arXiv preprint arXiv:2103.15388, 2021
Nonlinear wasserstein distributionally robust optimal control
Z Zhong, JJ Zhu
arXiv preprint arXiv:2304.07415, 2023
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