Research interests
My overall goal is to advance research of computational algorithms, using principles in applied mathematics and physics, to change the world for the better. In general, I am interested in optimization, machine learning, dynamical systems, and control theory. On one hand, I am motivated by addressing the lack of robustness and data distribution shift issues in modern learning algorithms. On the other hand, I am interested in interfacing dynamical systems (e.g., gradient flow, optimal transport, feedback control theory) and machine learning (e.g., robustness of deep learning models, generative models), aiming at building robust and scalable optimization and learning algorithms. All those call for a new generation of computational algorithms that can manipulate probability distributions and large-scale data structures robustly. Some example technical topics include
- robust machine learning, learning under distribution shift
- distributionally robust optimization, optimization under uncertainty
- generative models, machine learning applications of optimal transport and kernel methods
- numerical optimization, numerical methods
- data-driven modeling of dynamical systems and physics
- control theory, optimal control, multi-stage decision-making
Previous projects
- Kernel machine learning for distributionally robust optimization, Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen
- Marie Skołodowska-Curie Individual Fellowship on learning-control algorithms, Max Planck Institute for Intelligent Systems, Tübingen
- (More under construction …)
Publications, preprints, code
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, and Bernard Schölkopf. In the Proceedings of the 39th International Conference on Machine Learning (ICML). PMLR, 2022. paper poster
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu. To appear in the 61st IEEE Conference on Decision and Control (CDC). preprint code (summer school exercises) slides (summer school)
Adversarially Robust Kernel Smoothing. Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, volume 151 of Proceedings of Machine Learning Research, pages 4972–4994. PMLR, 28–30 Mar 2022. paper code slides (oral) poster
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics. Diego Agudelo-Espana, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu. To appear in the 61st IEEE Conference on Decision and Control (CDC). preprint
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions. Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu and Jan Peters. preprint
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control. Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu, 2021. Proceedings of the Conference on Learning for Dynamics and Control (L4DC). paper
Kernel Distributionally Robust Optimization. Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf, 2020. The conference version of this paper appeared in the Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, California, USA. PMLR: Volume 130. paper code slides (shorter version) (longer version)
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf, 2020. In the 59th IEEE Conference on Decision and Control (CDC)), 2020. paper slides
Projection Algorithms for Non-Convex Minimization with Application to Sparse Principal Component Analysis. J.J. Zhu, D. Phan, W. Hager, 2015. Journal of Global Optimization, 65(4):657–676, 2016. paper code
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. Zhu, Jia-Jie, Moritz Diehl and Bernhard Schölkopf. 2nd Annual Conference on Learning for Dynamics and Control (L4DC). In Proceedings of Machine Learning Research vol 120:1–9, 2020. paper slides
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. Zhu, Jia-Jie, Krikamol Muandet, Moritz Diehl, and Bernhard Schölkopf. 21st IFAC World Congress. In IFAC-PapersOnLine proceedings, 2020. paper slides
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control. Zhu, Jia-Jie, and Martius, Georg. 21st IFAC World Congress. In IFAC-PapersOnLine proceedings, 2020. paper slides
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning. Yeganegi, Mohammad Hasan, Majid Khadiv, S Ali A Moosavian, Jia-Jie Zhu, Andrea Del Prete, and Ludovic Righetti. IEEE Humanoids, 2019. paper
Generative Adversarial Active Learning. J.J. Zhu, J. Bento, 2017. NIPS 2017 Workshop on Teaching Machines, Robots, and Humans. paper
Control What You Can: Intrinsically Motivated Task-Planning Agent. Blaes, Sebastian, Marin Vlastelica Pogančić, JJ Zhu, and Georg Martius. In Advances in Neural Information Processing Systems (NeurIPS) 32, pages 12541– 12552. Curran Associates, Inc., 2019. paper
Deep Reinforcement Learning for Resource-Aware Control. D. Baumann, J.J. Zhu, G. Martius, S. Trimpe, 2018. IEEE CDC 2018. paper code
A Metric for Sets of Trajectories that is Practical and Mathematically Consistent. J. Bento, J.J. Zhu, 2016. paper
A Decentralized Multi-Block Algorithm for Demand-Side Primary Frequency Control Using Local Frequency Measurements. J. Brooks, W. Hager, J.J. Zhu, 2015. paper