Yuxiao Wen

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Yuxiao Wen (top)

I am a Ph.D. candidate in Computer Science in Courant Institute of Mathematical Sciences at New York University. I am fortunate to be advised by Prof. Yanjun Han and Prof. Zhengyuan Zhou. My research is mainly on learning theory, with a particular focus on bandit problems.

In 2021-23, I have been working on Scientific Machine Learning with Prof. Benjamin Peherstorfer. Prior to that, I received my B.A.s in Math and in CS at New York University and graduated with latin honor and department award (cf. my CV).

More about myself: I enjoy playing poker and city-walking in spare time. I love anything with matcha. My favorite anime/manga are (alphabetical order) Ajin, Alice in Borderland, Fire Punch, Jojo 2-4, The Disastrous Life of Saiki K., and tons of others.

Links to LinkedIn and Google Scholar.

You can contact me via email: yuxiaowen@nyu.edu

Interests
  • Bandit problems
  • Learning theory
Education

  • Ph.D. in CS, New York University, 2021-present

  • B.A. in CS, New York University, 2017-2021

  • B.A. in Honors Math, New York University, 2017-2021

publications

  1. How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning
    Arthur Jacot ,  Seok Hoan Choi ,  and  Yuxiao Wen
    arXiv, 2024
  2. Stochastic contextual bandits with graph feedback: from independence number to MAS number
    Yuxiao Wen ,  Yanjun Han ,  and  Zhengyuan Zhou
    Neural Information Processing Systems (NeurIPS), 2024
  3. Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
    Yuxiao Wen ,  and  Arthur Jacot
    International Conference on Machine Learning (ICML), 2024
  4. Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes
    Yuxiao Wen ,  Eric Vanden-Eijnden ,  and  Benjamin Peherstorfer
    Physica D, 2024
  5. Active operator inference for learning low-dimensional dynamical-system models from noisy data
    Wayne Isaac Tan Uy ,  Yuepeng Wang ,  Yuxiao Wen ,  and  Benjamin Peherstorfer
    SIAM Journal on Scientific Computing, 2023

teaching assistant