Research

My main interests have been in the intersection of three technical fields: optimization, dynamical systems and control, and machine learning, underpinned by principled applied math. One of my recent focuses is on data-driven robust optimization and control using tools such as functional/numerical analysis, principled machine learning and kernel methods. I believe mathematical optimization, such as modern stochastic and nonlinear programming, could play an important role in many cutting-edge applications such as large-scale machine learning and data-driven robust control.

Papers

Adversarially Robust Kernel Smoothing. Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf, 2021. working paper code (coming soon)

Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics. Diego Agudelo-Espana, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu. working paper

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. To appear in the 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. Proceedings of the 24thInternational Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego,California, USA. PMLR: Volume 130. paper code slides

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