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A robust assessment for invariant representations
The performance of machine learning models can be impacted by changes in data over time.
Statistical inference for pairwise comparison models
Pairwise comparison models have been widely used for utility evaluation and ranking across various fields.
Conformal Inference for Invariant Risk Minimization
The application of machine learning models can be significantly impeded by the occurrence of distributional shifts, as the assumption of homogeneity between the population of training and testing samples in machine learning and statistics may not be feasible in practical situations.
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction
We also derive non-asymptotic upper bounds for the difference of the lengths between the proposed non-crossing conformal prediction interval and the theoretically oracle prediction interval.
