2 citations · 5 across the 12 of their papers we have counts for
13 papers
Poisson empirical Bayes estimation of sums of random variables via minimum-distance methods
Stefano Favaro, Sandra Fortini, Soham Jana
The estimation of sums of functions of observable and unobservable variables is a long-standing problem in statistics, with applications in many domains. We consider this problem i…
Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression
Soham Jana
Speckle noise is a multiplicative noise commonly encountered in coherent imaging modalities such as synthetic aperture radar, optical coherence tomography, and digital holography.…
Factor Informed Double Deep Learning For Average Treatment Effect Estimation
Jianqing Fan, Soham Jana, Sanjeev Kulkarni +1
We investigate the problem of estimating the average treatment effect (ATE) under a very general setup where the covariates can be high-dimensional, highly correlated, and can have…
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…
Multilook Coherent Imaging: Theoretical Guarantees and Algorithms
Xi Chen, Soham Jana, Christopher A. Metzler +2
Multilook coherent imaging is a widely used technique in applications such as digital holography, ultrasound imaging, and synthetic aperture radar. A central challenge in these sys…
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…
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…
Empirical Bayes via ERM and Rademacher complexities: the Poisson model
Soham Jana, Yury Polyanskiy, Anzo Teh +1
We consider the problem of empirical Bayes estimation for (multivariate) Poisson means. Existing solutions that have been shown theoretically optimal for minimizing the regret (exc…
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…
Support Recovery with Stochastic Gates: Theory and Application for Linear Models
Soham Jana, Henry Li, Yutaro Yamada +1
Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise. We study the problem of estimati…
Optimal prediction of Markov chains with and without spectral gap
Yanjun Han, Soham Jana, Yihong Wu
We study the following learning problem with dependent data: Observing a trajectory of length from a stationary Markov chain with states, the goal is to predict the next st…
Extrapolating the profile of a finite population
Soham Jana, Yury Polyanskiy, Yihong Wu
We study a prototypical problem in empirical Bayes. Namely, consider a population consisting of individuals each belonging to one of types (some types can be empty). Withou…