Guided Adversarial Robust Transfer Learning with Source Mixing

arXiv:2309.06534 · cs.LG, stat.ME · Submitted 2023-09-12 · Read on arXiv

cs.LG, stat.ME

Submitted: 2023-09-12

Updated: 2026-09-08

Journal ref: Journal of the American Statistical Association (2026)

DOI: 10.1080/01621459.2026.2675620

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

Terminology

• Transfer Learning — 19× in this paper · explained in 10 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Loss Function — 7× in this paper · explained in 2 episodes
• Generalizability — 5× in this paper · explained in 6 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Sparsity — 4× in this paper · explained in 4 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Guided Adversarial Robust Transfer — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Mass General Brigham — 1× in this paper
• Minimax optimality — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
• Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation

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