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math.ST2026
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…
math.ST2026
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…
math.ST2026
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…