Morris Yau


Morris Yau Ph.D Candidate MIT Computer Science

I am Morris a Ph.D Candidate at MIT in EECS advised by Prof. Jacob Andreas, Prof. Stefanie Jegelka, and Prof. Ankur Moitra. My research is broadly in the area of Artificial Intelligence, with a particular focus on the algorithmic foundations of language and intelligence. Towards these ends, I am currently focused on studying neural networks as computational artifacts. How do we build models that compute solutions to intractable problems? What are the precise building blocks of language, and how can we manipulate them to ensure efficient generalization? Can we build intelligent systems with reasoning capabilities well beyond human cognition?

I received my masters in computer science from UC Berkeley under the wonderful supervision of Prof. Prasad Raghavendra studying the foundations of approximation. And before that I was an undergraduate at Harvard University where I had the great fortune to work with Prof. Madhu Sudan on aspects of algebraic complexity.

Manuscripts

Author order alphabetical, *indicates first authorship

  • Learning Linear Attention in Polynomial Time
    Authors: Morris Yau*, Eykin Akyurek, Jiayuan Mao, Joshua B. Tenenbaum, Stefanie Jegelka, Jacob Andreas
    Arxiv Link: https://arxiv.org/abs/2410.10101

Publications

  • Are Graph Neural Networks Optimal Approximation Algorithms? Authors: Morris Yau*, Eric Lu, Nikolaos Karalias, Jessica Xu, Stefanie Jegelka
    Venue: (Spotlight) Neurips 2024
    Arxiv Link: https://arxiv.org/abs/2310.00526
  • Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
    Authors: Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
    Venue: 40th ICML
  • A New Approach To Learning Linear Dynamical Systems
    Authors: Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
    Venue: 55th Annual ACM Symposium on Theory of Computing (STOC 2023)
    Arxiv Link: https://arxiv.org/abs/2301.09519
  • Kalman Filtering with Adversarial Corruptions
    Authors: Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    Venue: 54th Annual ACM Symposium on Theory of Computing (STOC 2022)
    Arxiv Link: https://arxiv.org/pdf/2111.06395.pdf
  • Online and Distribution Free Robustness: Regression and Contextual Bandits with Huber Contamination
    Authors: Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    Venue: Proceedings of the 62nd Annual IEEE Symposium on Foundations of Computer Science (FOCS 2021)
    Arxiv Link: https://arxiv.org/abs/2010.04157
  • Classification under Misspecfication: Halfspaces, Generalized Linear Models, and Connections to Evolvability
    Authors: Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    Venue: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Spotlight
    Arxiv Link: https://arxiv.org/abs/2006.04787
  • List Decodable Mean Estimation in Nearly Linear Time
    Authors: Yeshwanth Cherapanamjeri, Sidhanth Mohanty, Morris Yau
    Venue: Proceedings of the 61st Annual IEEE Symposium on Foundations of Computer Science (FOCS 2020)
    Arxiv Link: https://arxiv.org/abs/2005.09796
  • List Decodable Subspace Recovery
    Authors: Prasad Raghavendra and Morris Yau
    Venue: The 33rd Annual Conference on Learning Theory (COLT 2020)
    Arxiv Link: https://arxiv.org/abs/2002.03004
  • List Decodable Learning via Sum of Squares
    Authors: Prasad Raghavendra and Morris Yau
    Venue: Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2020)
    Arxiv Link: https://arxiv.org/abs/1905.04660