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Creators/Authors contains: "Xin, Cheng"

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  1. ; ; ; ; (, Proceedings of the 42nd International Conference on Machine Learning (ICML 2025))
    Free, publicly-accessible full text available July 14, 2026
  2. ; (, https://arxiv.org/abs/2403.08110)
    Free, publicly-accessible full text available May 18, 2026
  3. ; ; ; ; ; (, NIPS '24: Proceedings of the 38th International Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 22, 2025
  4. ; ; ; ; ; (, The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS))
    We introduce Non-Euclidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symmetric bilinear forms to utilize the negative eigenvalues of dissimilarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS, the sum of squared pairwise error. We provide an in-depth error analysis and proofs of the optimality in minimizing lower bounds of STRESS. We demonstrate Neuc-MDS’s ability to address limitations of classical MDS raised by prior research, and test it on various synthetic and real-world datasets in comparison with both linear and non-linear dimension reduction methods. 
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    Free, publicly-accessible full text available December 9, 2025
  5. ; ; (, Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024)
    Accepted Manuscript Full Text Available
  6. ; ; (, International Conference on Machine Learning (ICML 2024))
    Accepted Manuscript Full Text Available
  7. ; ; (, Proceedings of the 41st International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, https://arxiv.org/abs/2406.07100)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; (, https://arxiv.org/abs/2406.07100)
    Accepted Manuscript Full Text Available
  10. ; (, j34th International Symposium on Computational Geometry (SoCG 2018))
    Computation of the interleaving distance between persistence modules is a central task in topological data analysis. For $$1$$-D persistence modules, thanks to the isometry theorem, this can be done by computing the bottleneck distance with known efficient algorithms. The question is open for most $$n$$-D persistence modules, $n>1$$, because of the well recognized complications of the indecomposables. Here, we consider a reasonably complicated class called {\em $$2$$-D interval decomposable} modules whose indecomposables may have a description of non-constant complexity. We present a polynomial time algorithm to compute the bottleneck distance for these modules from indecomposables, which bounds the interleaving distance from above, and give another algorithm to compute a new distance called {\em dimension distance} that bounds it from below. 
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    Accepted Manuscript Full Text Available