Environment-Conditioned Tail Reweighting for Invariant Learning under Mixed Shifts

arXiv:2601.22944 · cs.LG · Submitted 2026-01-30 · Read on arXiv

cs.LG

Submitted: 2026-01-30

Updated: 2026-09-26

Comments: 9 pages

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

Terminology

• Generalization — 28× in this paper · explained in 13 episodes
• Robustness — 28× in this paper · explained in 5 episodes
• Invariant Risk Minimization — 24× in this paper
• Benchmark — 21× in this paper · explained in 2 episodes
• Distribution Shift — 10× in this paper · explained in 5 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Regularization — 7× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 5× in this paper
• Aggregation — 4× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 3× in this paper
• Unified Framework — 3× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Degeneracy — 2× in this paper · explained in 3 episodes
• Homogeneity — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Agnostic Learning — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes

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