26 citations · 56 across the 21 of their papers we have counts for
9 papers · 1 filter
PDE-constrained inverse problems at the rate via debiased physics-informed neural networks
Yves Atchade, Debarghya Mukherjee
We study the problem of estimating unknown parameters in PDE-constrained inverse problems from noisy observations, where the PDE solution is approximated using Physics-Informed Neu…
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
Radu Lecoiu, Debarghya Mukherjee, Pragya Sur
Self-distillation has emerged as a promising technique for improving model performance in modern machine learning systems. We develop the statistical foundations of self-distillati…
Minimax optimal adaptive structured transfer learning through semi-parametric domain-varying coefficient model
Hanxiao Chen, Debarghya Mukherjee
Transfer learning aims to improve inference in a target domain by leveraging information from related source domains, but its effectiveness critically depends on how cross-domain h…
On the estimation rate of Bayesian PINN for inverse problems
Yi Sun, Debarghya Mukherjee, Yves Atchade
Solving partial differential equations (PDEs) and their inverse problems using Physics-informed neural networks (PINNs) is a rapidly growing approach in the physics and machine lea…
Minimax Optimal rates of convergence in the shuffled regression, unlinked regression, and deconvolution under vanishing noise
Cecile Durot, Debarghya Mukherjee
Shuffled regression and unlinked regression represent intriguing challenges that have garnered considerable attention in many fields, including but not limited to ecological regres…
Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimension
Sohom Bhattacharya, Jianqing Fan, Debarghya Mukherjee
Deep neural networks have achieved tremendous success due to their representation power and adaptation to low-dimensional structures. Their potential for estimating structured regr…
On robust learning in the canonical change point problem under heavy tailed errors in finite and growing dimensions
Debarghya Mukherjee, Moulinath Banerjee, Ya'acov Ritov
This paper presents a number of new findings about the canonical change point estimation problem. The first part studies the estimation of a change point on the real line in a simp…
Asymptotic normality of a linear threshold estimator in fixed dimension with near-optimal rate
Debarghya Mukherjee, Moulinath Banerjee, Debasri Mukherjee +1
Linear thresholding models postulate that the conditional distribution of a response variable in terms of covariates differs on the two sides of a (typically unknown) hyperplane in…
Optimal Linear Discriminators For The Discrete Choice Model In Growing Dimensions
Debarghya Mukherjee, Moulinath Banerjee, Ya'acov Ritov
Manski's celebrated maximum score estimator for the discrete choice model, which is an optimal linear discriminator, has been the focus of much investigation in both the econometri…