Grigorios Chrysos

Assistant Professor


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Room: M1002B
Engineering Centers Building

Profile Summary

My research focuses on reliable machine learning and the design and study of expressive models that are robust to noise and generalize well in out-of-distribution data. Concretely:

  • I am interested in understanding the inducative bias of deep networks and properties of existing architectures through empirical and theoretical studies. I am interested in the complete theoretical understanding of (neural/polynomial) networks, including their expressivity, trainability, generalization properties
  • The understanding of the inductive bias will enable us to design improved networks. Towards that end, I have worked extensively on polynomial networks (PNs). PNs that capture high-degree interactions between inputs.
  • I am interested in the extrapolation properties of existing networks and improving their performance, especially in the context of conditional generative models. In the short-term, I will continue to explore the robustness of these models to malicious attacks, as well as the impact of adversarial perturbations on different classes. In the long-term, I plan to design models that are both robust and fair, and can generalize well to unseen combinations.


I am looking for (under)graduate students who are passionate in working in machine learning. Before you e-mail me, please check out the related section in my site.

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