Hi, this is J.J. Zhu. I am a postdoctoral researcher at the Max Planck Institute for Intelligent Systems, at the Empirical Inference Department.

News and updates

March, 2021.

A couple of preprints and accepted manuscripts in distributional robustness in optimization, learning, control:

  • From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing. Jia-Jie Zhu, Yassine Nemmour, Bernhard Schölkopf, 2021. 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). PMLR. paper (coming soon)

  • 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

Research interests

In general, I am an applied mathematician interested in optimization, function spaces, probability, data, systems, and control. More specifically, I am interested in the following technical topics.

  • (Distributionally) Robust optimization, stochastic optimization
  • Numerical analysis, numerical optimization
  • Machine learning, kernel methods
  • Stochastic systems, control, reinforcement learning

learning and control