5 citations · 30 across the 11 of their papers we have counts for
11 papers
Bayes optimal learning in high-dimensional linear regression with network side information
Sagnik Nandy, Subhabrata Sen
Supervised learning problems with side information in the form of a network arise frequently in applications in genomics, proteomics and neuroscience. For example, in genetic appli…
Random linear estimation with rotationally-invariant designs: Asymptotics at high temperature
Yufan Li, Zhou Fan, Subhabrata Sen +1
We study estimation in the linear model , in a Bayesian setting where has an entrywise i.i.d. prior and the design is rotationally-invariant in law. In…
Sparse Signal Detection in Heteroscedastic Gaussian Sequence Models: Sharp Minimax Rates
Julien Chhor, Rajarshi Mukherjee, Subhabrata Sen
Given a heterogeneous Gaussian sequence model with unknown mean and known covariance matrix , we study the signal det…
Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing Matrices
Rishabh Dudeja, Subhabrata Sen, Yue M. Lu
It has been observed that the performances of many high-dimensional estimation problems are universal with respect to underlying sensing (or design) matrices. Specifically, matrice…
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond
Kuanhao Jiang, Rajarshi Mukherjee, Subhabrata Sen +1
Estimation of the average treatment effect (ATE) is a central problem in causal inference. In recent times, inference for the ATE in the presence of high-dimensional covariates has…
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
Tengyuan Liang, Subhabrata Sen, Pragya Sur
We study Langevin dynamics for recovering the planted signal in the spiked matrix model. We provide a "path-wise" characterization of the overlap between the output of the Langevin…
Universality of Approximate Message Passing with Semi-Random Matrices
Rishabh Dudeja, Yue M. Lu, Subhabrata Sen
Approximate Message Passing (AMP) is a class of iterative algorithms that have found applications in many problems in high-dimensional statistics and machine learning. In its gener…
A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists
Andrea Montanari, Subhabrata Sen
This tutorial is based on lecture notes written for a class taught in the Statistics Department at Stanford in the Winter Quarter of 2017. The objective was to provide a working kn…
The TAP free energy for high-dimensional linear regression
Jiaze Qiu, Subhabrata Sen
We derive a variational representation for the log-normalizing constant of the posterior distribution in Bayesian linear regression with a uniform spherical prior and an i.i.d. Gau…
TAP equations for orthogonally invariant spin glasses at high temperature
Zhou Fan, Yufan Li, Subhabrata Sen
We study the high-temperature regime of a mean-field spin glass model whose couplings matrix is orthogonally invariant in law. The magnetization of this model is conjectured to sat…
Regret Minimization in Isotonic, Heavy-Tailed Contextual Bandits via Adaptive Confidence Bands
Sabyasachi Chatterjee, Subhabrata Sen
In this paper we initiate a study of non parametric contextual bandits under shape constraints on the mean reward function. Specifically, we study a setting where the context is on…