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Creators/Authors contains: "Zeng, Yuchen"

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  1. ; ; ; ; ( , International Conference on Learning Representations (ICLR))
  2. ; ; ; ; ( , International Conference on Learning Representations (ICLR) 2023)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ( , 2021 IEEE International Conference on Image Processing (ICIP))
    Full Text Available 
  4. ; ( , Advances in Neural Information Processing Systems 32 (NeurIPS))
    We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization. We propose a tensor block model, develop a unified least-square estimation, and obtain the theoretical accuracy guarantees for multiway clustering. The statistical convergence of the estimator is established, and we show that the associated clustering procedure achieves partition consistency. A sparse regularization is further developed for identifying important blocks with elevated means. The proposal handles a broad range of data types, including binary, continuous, and hybrid observations. Through simulation and application to two real datasets, we demonstrate the outperformance of our approach over previous methods. 
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    Accepted Manuscript Full Text Available