Rising Stars 2020:

Xinyi Chen

PhD Candidate

Princeton University

Areas of Interest

  • Artificial Intelligence
  • Control, Intelligent Systems, and Robotics


Black-Box Control for Linear Dynamical Systems


We consider the problem of controlling an unknown linear time-invariant dynamical system from a single chain of black-box interactions, and with no access to resets or offline simulation. Under the assumption that the system is controllable, we give the first efficient algorithm that is capable of attaining sublinear regret in a single trajectory under the setting of online nonstochastic control. We give finite-time regret bound of our algorithm, as well as a nearly-matching lower bound that shows this regret to be almost best-attainable by any deterministic algorithm.


Xinyi Chen is a PhD student in the department of computer science at Princeton University, advised by Professor Elad Hazan. Prior to this, she received her undergraduate degree in mathematics from the same institution. Her research interests lie at the intersection of online learning, theoretical machine learning, and control. She is a founding member of the Google AI Princeton lab, and is a recipient of the NSF Graduate Research Fellowship Award and the Gordon Wu Fellowship at Princeton University.

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