| A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees | Jun 6, 2022 | Bayesian InferenceModel Predictive Control | —Unverified | 0 | 0 |
| A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems | Dec 28, 2022 | Uncertainty Quantification | —Unverified | 0 | 0 |
| Algorithms with Calibrated Machine Learning Predictions | Feb 5, 2025 | SchedulingUncertainty Quantification | —Unverified | 0 | 0 |
| Aligned Multi-Task Gaussian Process | Oct 29, 2021 | Bayesian InferenceGaussian Processes | —Unverified | 0 | 0 |
| All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty | Feb 24, 2025 | Allcounterfactual | —Unverified | 0 | 0 |
| A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty | Jun 10, 2024 | Uncertainty Quantification | —Unverified | 0 | 0 |
| AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations | Feb 6, 2025 | Active LearningComputational Efficiency | —Unverified | 0 | 0 |
| Alternating linear scheme in a Bayesian framework for low-rank tensor approximation | Dec 21, 2020 | Bayesian InferenceTensor Decomposition | —Unverified | 0 | 0 |
| A machine learning approach for efficient uncertainty quantification using multiscale methods | Nov 12, 2017 | AttributeBIG-bench Machine Learning | —Unverified | 0 | 0 |
| A Model-Constrained Discontinuous Galerkin Network (DGNet) for Compressible Euler Equations with Out-of-Distribution Generalization | Sep 27, 2024 | Out-of-Distribution GeneralizationUncertainty Quantification | —Unverified | 0 | 0 |
