A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
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Causal stability selection produces effect-modifier sets with explicit non-asymptotic false-positive bounds by combining cross-fitted CATE estimation and integrated path stability selection.
A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
Di-COT is an unsupervised contrastive method that stochastically partitions time-series windows into overlapping sub-blocks to learn representations without augmentation, reporting SOTA results on classification and transfer tasks across multiple benchmarks while cutting training time.
Introduces nLoI and four complementary divergence measures with within/between-node decomposition and unified permutation testing to evaluate surrogate reconstruction quality for Explainable Ensemble Trees.
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
DKPS-based methods predict new model benchmark scores using cached responses, matching baseline mean absolute error with substantially fewer queries and an offline query selection approach.
Dual-Glob applies supervised contrastive learning to classify fine-grained pitch accent patterns from F0 contours in Seoul Korean, achieving 77.75% accuracy and 51.54% F1 on a new dataset of 10,093 manually annotated accentual phrases.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
Graph-augmented LLMs using a political knowledge graph improve ideology prediction accuracy for Swiss MPs by incorporating relational data beyond text alone.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.
TabGRAA applies group-relative advantage alignment in an iterative reward-guided post-training loop to improve tabular language model generators on fidelity, utility, and privacy trade-offs across five benchmarks.
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.
The paper introduces a question-driven framework and set of statistical methods for exploratory assessment of regional treatment effect heterogeneity in multi-regional clinical trials, evaluated via simulations under no-heterogeneity and modifier-driven scenarios.
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Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.