Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learning

arXiv:2408.16189 · stat.ML, cs.AI, cs.LG, math.ST, stat.TH · Submitted 2024-08-29 · Read on arXiv

stat.ML, cs.AI, cs.LG, math.ST, stat.TH

Submitted: 2024-08-29

Updated: 2026-09-25

Terminology

• Adaptation — 19× in this paper · explained in 1 episode
• Transfer Learning — 18× in this paper · explained in 10 episodes
• Domain Adaptation — 14× in this paper · explained in 5 episodes
• Bernstein Class Condition — 7× in this paper
• Adaptivity — 6× in this paper · explained in 1 episode
• VC Dimension — 6× in this paper · explained in 2 episodes
• Covariate Shift — 4× in this paper · explained in 2 episodes
• Transferability — 4× in this paper · explained in 4 episodes
• Wasserstein Distance — 4× in this paper · explained in 4 episodes
• Feature Selection — 3× in this paper · explained in 4 episodes
• Hamming Distance — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Meta-learning — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Admissibility — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Domain Shift — 1× in this paper · explained in 4 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• On the ERM Principle in Meta-Learning
• Invariant Risk Minimization
• High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
• Few-Shot Learning via Learning the Representation, Provably
• Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
• Nonlinear Meta-Learning Can Guarantee Faster Rates
• Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints
• On the Theory of Transfer Learning: The Importance of Task Diversity
• Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
• On Localized Discrepancy for Domain Adaptation

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