26 citations · 56 across the 21 of their papers we have counts for
22 papers
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…
Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
Hanxiao Chen, Debarghya Mukherjee
We study clustered multitask learning in a semiparametric setting where tasks share a latent cluster structure in their target parameters but exhibit heterogeneous, potentially inf…
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…
CINDES: Classification induced neural density estimator and simulator
Dehao Dai, Jianqing Fan, Yihong Gu +1
Neural network-based methods for (un)conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical app…
Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model
Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee +1
In many practical situations, randomly assigning treatments to subjects is uncommon due to feasibility constraints. For example, economic aid programs and merit-based scholarships…
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…
Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift
Jiawei Ge, Debarghya Mukherjee, Jianqing Fan
As machine learning models are increasingly deployed in dynamic environments, it becomes paramount to assess and quantify uncertainties associated with distribution shifts. A distr…
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…
UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
Jianqing Fan, Jiawei Ge, Debarghya Mukherjee
Uncertainty quantification in prediction presents a compelling challenge with vast applications across various domains, including biomedical science, economics, and weather forecas…
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…
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Subha Maity, Debarghya Mukherjee, Moulinath Banerjee +1
Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most pr…
Domain Adaptation meets Individual Fairness. And they get along
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresente…
Post-processing for Individual Fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun +1
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it…
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…
Estimation of a score-explained non-randomized treatment effect in fixed and high dimensions
Debarghya Mukherjee, Moulinath Banerjee, Ya'acov Ritov
Non-randomized treatment effect models are widely used for the assessment of treatment effects in various fields and in particular social science disciplines like political science…
Outlier-Robust Optimal Transport
Debarghya Mukherjee, Aritra Guha, Justin Solomon +2
Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT dista…
Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training da…
Markovian And Non-Markovian Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic
Hamid Eftekhari, Debarghya Mukherjee, Moulinath Banerjee +1
We study and predict the evolution of Covid-19 in six US states from the period May 1 through August 31 using a discrete compartment-based model and prescribe active intervention p…
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee +1
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific f…
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…