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From symmetry to geometry: Tractable nonconvex problems
As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …
has expanded to touch almost every stage of the data analysis pipeline, from signal and …
Leave-one-out singular subspace perturbation analysis for spectral clustering
In the supplement [46], we first provide the proof of Theorem 2.3 in Appendix A, followed by
the proofs of results of Section 3.4 in Appendix B. The proof of Theorem 3.3 is given in …
the proofs of results of Section 3.4 in Appendix B. The proof of Theorem 3.3 is given in …
Clustering a mixture of gaussians with unknown covariance
We investigate a clustering problem with data from a mixture of Gaussians that share a
common but unknown, and potentially ill-conditioned, covariance matrix. We start by …
common but unknown, and potentially ill-conditioned, covariance matrix. We start by …
Statistical-computational trade-offs in tensor pca and related problems via communication complexity
Statistical-computational trade-offs in tensor PCA and related problems via communication
complexity Page 1 The Annals of Statistics 2024, Vol. 52, No. 1, 131–156 https://doi.org/10.1214/23-AOS2331 …
complexity Page 1 The Annals of Statistics 2024, Vol. 52, No. 1, 131–156 https://doi.org/10.1214/23-AOS2331 …
High-dimensional estimation, basis assets, and the adaptive multi-factor model
The paper proposes a new algorithm for the high-dimensional financial data—the
Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi …
Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi …
A new variational model for shape graph registration with partial matching constraints
This paper introduces a new extension of Riemannian elastic curve matching to a general
class of geometric structures, which we call (weighted) shape graphs, that allows for shape …
class of geometric structures, which we call (weighted) shape graphs, that allows for shape …
Optimal clustering in anisotropic gaussian mixture models
Clustering structure of microstructure measures
This paper builds the clustering model of measures of market microstructure features which
are popular in predicting stock returns. In a 10-second time-frequency, we study the …
are popular in predicting stock returns. In a 10-second time-frequency, we study the …
Achieving optimal clustering in Gaussian mixture models with anisotropic covariance structures
X Chen, AY Zhang - The Thirty-eighth Annual Conference on Neural …, 2024 - openreview.net
We study clustering under anisotropic Gaussian Mixture Models (GMMs), where covariance
matrices from different clusters are unknown and are not necessarily the identity matrix. We …
matrices from different clusters are unknown and are not necessarily the identity matrix. We …