Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting

arXiv:2607.26458 · stat.ML, cs.LG, math.ST, stat.ME, stat.TH · Submitted 2026-07-29 · Read on arXiv

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

Submitted: 2026-07-29

Updated: 2026-09-22

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

Terminology

• Accuracy — 105× in this paper · explained in 1 episode
• Generalization — 20× in this paper · explained in 14 episodes
• Ablation — 17× in this paper · explained in 1 episode
• Robustness — 14× in this paper · explained in 6 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Disentanglement — 7× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 3 episodes
• Wasserstein Distance — 4× in this paper · explained in 6 episodes
• Composition — 3× in this paper · explained in 2 episodes
• Degeneracy — 2× in this paper · explained in 4 episodes
• Distribution Shift — 2× in this paper · explained in 12 episodes
• Distribution Shifts — 2× in this paper · explained in 2 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Domain Shift — 2× in this paper · explained in 7 episodes
• Hyperparameters — 2× in this paper · explained in 2 episodes
• Information Bottleneck — 2× in this paper · explained in 3 episodes
• Latent Adaptive Domain Disentanglement and Environment Reweighting — 2× in this paper
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Training Objective — 2× in this paper · explained in 3 episodes
• Transferability — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Class Imbalance — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 4 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 4 episodes
• Hypernetwork — 1× in this paper · explained in 4 episodes
• Linear Probe — 1× in this paper · explained in 3 episodes
• Monotonicity — 1× in this paper · explained in 2 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
• Invariant Risk Minimization
• Multi-dimensional domain generalization with low-rank structures
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Beyond Model Adaptation at Test Time: A Survey
• A Survey of Unsupervised Domain Adaptation for Visual Recognition
• Domain Generalization Under Posterior Drift
• Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction

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