From the 1 of 7 linked papers with an AI index.
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Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression
Soham Jana
The paper develops a minimax theory for likelihood‑based deep neural network estimators in speckle regression, showing they achieve optimal nonparametric rates despite multiplicati…
Minimax Analysis of Estimation Problems in Coherent Imaging
Hao Xing, Soham Jana, Arian Maleki
Unlike conventional imaging modalities, such as magnetic resonance imaging, which are often well described by a linear regression framework, coherent imaging systems follow a signi…
Adversarially robust clustering with optimality guarantees
Soham Jana, Kun Yang, Sanjeev Kulkarni
We consider the problem of clustering data points coming from sub-Gaussian mixtures. Existing methods that provably achieve the optimal mislabeling error, such as the Lloyd algorit…
Optimal empirical Bayes estimation for the Poisson model via minimum-distance methods
Soham Jana, Yury Polyanskiy, Yihong Wu
The Robbins estimator is the most iconic and widely used procedure in the empirical Bayes literature for the Poisson model. On one hand, this method has been recently shown to be m…
A provable initialization and robust clustering method for general mixture models
Soham Jana, Jianqing Fan, Sanjeev Kulkarni
Clustering is a fundamental tool in statistical machine learning in the presence of heterogeneous data. Most recent results focus primarily on optimal mislabeling guarantees when d…
Factor Adjusted Spectral Clustering for Mixture Models
Shange Tang, Soham Jana, Jianqing Fan
This paper studies a factor modeling-based approach for clustering high-dimensional data generated from a mixture of strongly correlated variables. Statistical modeling with correl…