Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Transfer learning for high-dimensional expectile regression
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.
Proposes an adaptive transfer LASSO quantile estimator incorporating source data via penalties, claiming consistency, sparsity, convergence rates, and an algorithm for computation, validated on simulations and protein data.
citing papers explorer
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Bayesian Multivariate Sparse Functional Principal Components Analysis
MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
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Transfert learning and adaptive LASSO quantile
Proposes an adaptive transfer LASSO quantile estimator incorporating source data via penalties, claiming consistency, sparsity, convergence rates, and an algorithm for computation, validated on simulations and protein data.