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Creators/Authors contains: "Chen, Ziang"

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  1. ; (, NeurIPS 2024)
    In this work, we study the mean-field flow for learning subspace-sparse polynomials using stochastic gradient descent and two-layer neural networks, where the input distribution is standard Gaussian and the output only depends on the projection of the input onto a low-dimensional subspace. We establish a necessary condition for SGD-learnability, involving both the characteristics of the target function and the expressiveness of the activation function. In addition, we prove that the condition is almost sufficient, in the sense that a condition slightly stronger than the necessary condition can guarantee the exponential decay of the loss functional to zero. 
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    Free, publicly-accessible full text available December 15, 2025
  2. ; ; ; (, Mathematics of Computation)
    Free, publicly-accessible full text available November 27, 2025
  3. ; ; (, SIAM Journal on Matrix Analysis and Applications)
    Full Text Available 
  4. ; ; ; (, SIAM Journal on Numerical Analysis)
    Full Text Available 
  5. ; ; (, Communications in Mathematical Sciences)
    Full Text Available 
  6. ; ; (, SIAM Journal on Optimization)
    Full Text Available 
  7. ; ; ; (, SIAM Journal on Mathematical Analysis)
    Full Text Available 
  8. ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  9. ; ; (, Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available