arXiv:2606. 18993v1 Announce Type: cross Abstract: Testing conditional independence is fundamental yet intrinsically difficult: without additional assumptions, Type I error control is impossible in general.
arXiv:2512. 19510v2 Announce Type: replace Abstract: Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions.
arXiv:2607. 08522v1 Announce Type: new Abstract: The inherent rigidity of fixed-size benchmarks makes them an inefficient tool for model evaluation.
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit the informative predictions of powerful black--box models, even though such predictions are increasingly available in settings where labels remain expensive.
The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.
arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.
arXiv:2606. 08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.
arXiv:2602. 13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty.
arXiv:2606. 18011v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships.
arXiv:2608.27704v1 Announce Type: new Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destabilize online updates. We study robust test-time adaptation (RTTA) in the adversarial-stream setting, which remains comparatively underexplored relative to standard TTA, and propose SAFER (Stochastic Augmentation Framework for Enhanced Robustness), a training-free reliability-guided augmentation wrapper for RTTA.
arXiv:2602. 13848v2 Announce Type: replace Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting.
arXiv:2609.37687v1 Announce Type: new Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively queryin...