Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regression

arXiv:2609.15785 · stat.ML, cs.LG · Submitted 2026-09-14 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-14

Updated: 2026-09-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Regularization — 33× in this paper · explained in 3 episodes
Covariate Shift — 13× in this paper · explained in 2 episodes
Adaptation — 10× in this paper · explained in 1 episode
Hilbert Space — 7× in this paper · explained in 2 episodes
Consistency — 6× in this paper · explained in 3 episodes
Convergence Guarantees — 6× in this paper · explained in 3 episodes
Distribution Shift — 5× in this paper · explained in 6 episodes
Calibration — 4× in this paper · explained in 7 episodes
Inverse Problem — 4× in this paper · explained in 2 episodes
Kernel Mean Matching — 4× in this paper
Reproducing Kernel Hilbert Space — 4× in this paper
Domain Adaptation — 3× in this paper · explained in 5 episodes
Generalization — 3× in this paper · explained in 13 episodes
Accuracy — 2× in this paper · explained in 1 episode
Reproducing Kernel Hilbert Space (RKHS) — 2× in this paper · explained in 3 episodes
Black Box Shift Estimation — 1× in this paper
Curse of Dimensionality — 1× in this paper · explained in 6 episodes
Embeddings — 1× in this paper · explained in 2 episodes
Error Decomposition — 1× in this paper · explained in 3 episodes
Gradient Flow — 1× in this paper · explained in 2 episodes
Minimax Optimal Rates — 1× in this paper · explained in 2 episodes
Theoretical Guarantees — 1× in this paper · explained in 2 episodes