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The paper proposes CJSD, a method using two discriminators to exactly separate covariate shift from mechanism change in datasets, with theoretical guarantees and empirical results.
This paper introduces a cross-model map for certifying selective predictors that must meet both an automation floor and a risk ceiling under covariate shift, deriving feasibility frontiers and two-resource sample-complexity trade-offs.
CalTwin introduces a Fisher-Information-based regularization to improve robustness to covariate shift and confidence misalignment in medical world models, achieving modest improvements on the PhysioNet 2019 Sepsis Challenge dataset.
ReGuide introduces a self-improving framework for diffusion policies that uses test-time guidance to generate corrective rollouts, then fine-tunes the policy on this data, achieving 1.3–7.7× success improvement on Robomimic tasks.
This paper introduces the Expectation Consistency Loss (ECL), a theoretically grounded loss function for calibrating classifier confidence under covariate shift, derived from a necessary and sufficient condition called the Expectation Consistency Condition.
TILT introduces a novel objective for unsupervised domain adaptation under covariate shift that penalizes an auxiliary component on unlabeled target data, implicitly achieving self-localized importance weighting with bounded estimands. Theoretical guarantees and experiments on shifted CIFAR-100 show improved target performance over baselines.
This paper revisits Dataset Aggregation (DAgger) for training long-horizon LLM agents, demonstrating that turn-level teacher-student policy interpolation mitigates covariate shift and outperforms existing methods on software engineering benchmarks like SWE-bench Verified.