| Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors | May 14, 2020 | Uncertainty Quantification | —Unverified | 0 |
| Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems | Mar 20, 2025 | AstronomyBayesian Inference | —Unverified | 0 |
| Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion | Mar 13, 2023 | Physics-informed machine learningUncertainty Quantification | —Unverified | 0 |
| Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation | Sep 15, 2021 | Feature EngineeringLanguage Modeling | —Unverified | 0 |
| Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions | Dec 21, 2023 | Conformal Predictionquantile regression | —Unverified | 0 |
| Efficient Conformal Prediction under Data Heterogeneity | Dec 25, 2023 | Conformal PredictionFederated Learning | —Unverified | 0 |
| Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory | Jul 9, 2024 | Federated Learningparameter estimation | —Unverified | 0 |
| Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning | Apr 2, 2025 | MuJoCoUncertainty Quantification | —Unverified | 0 |
| An asymptotic analysis of distributed nonparametric methods | Nov 8, 2017 | Uncertainty Quantification | —Unverified | 0 |
| Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks | Jul 19, 2024 | Meta-LearningModel Predictive Control | —Unverified | 0 |
