Rising Stars 2020:

Anqi (Angie) Liu

Postdoctoral Researcher

California Institute of Technology

PhD '18 University of Illinois at Chicago

Areas of Interest

  • Artificial Intelligence


Machine Learning for the Real World: Distributionally Robust Extrapolation


The unprecedented prediction accuracy of modern machine learning beckons for its application in a wide range of real-world applications, including autonomous robots, medical decision-making, scientific experimental design, and many others. A key challenge in such real-world applications is that the test cases are not well represented by the pre-collected training data. To properly leverage learning in such domains, especially safety-critical ones, we must go beyond the conventional learning paradigm of maximizing average prediction accuracy with generalization guarantees that rely on strong distributional relationships between training and test examples.

In this poster, I will describe a distributionally robust learning framework that offers rigorous extrapolation guarantees under data distribution shift. This framework yields appropriately conservative yet still accurate predictions to guide real-world decision-making and is easily integrated with modern deep learning. I will showcase the practicality of this framework in applications on agile robotic control and computer vision. I will also introduce a survey of other real-world applications that would benefit from this framework for future work.


Anqi (Angie) Liu is a machine learning researcher and postdoctoral scholar at the Department of Computing and Mathematical Sciences in Caltech. She obtained her Ph.D. from the Department of Computer Science of the University of Illinois at Chicago. She works on distributionally robust learning, distribution shift, and interactive machine learning. She is interested in machine learning for safety-critical tasks and the social impact of AI. She aims to design learning methods for more reliable systems in the real world.

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