← Learning Types
Machine Learning › Learning Types ›

Uncertainty Quantification

663 directly classified papers

Papers per year

Papers

Uncertainty on Asynchronous Time Event Prediction NIPS 2019
Computing Full Conformal Prediction Set with Approximate Homotopy NIPS 2019
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks NIPS 2019
Accurate Layerwise Interpretable Competence Estimation NIPS 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty NIPS 2019
Conformalized Quantile Regression NIPS 2019
Modelling Uncertainty in Collaborative Document Quality Assessment EMNLP 2019
Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning AAAI 2019
Verified Uncertainty Calibration NIPS 2019
Noise Contrastive Priors for Functional Uncertainty UAI 2019
Quantifying Uncertainty in Online Regression Forests JMLR 2019
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models AISTATS 2019
Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera CVPR 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty NIPS 2019
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model NIPS 2019
Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences CVPR 2019
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction CVPR 2019
What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions CVPR 2019
Striking the Right Balance With Uncertainty CVPR 2019
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters NIPS 2019
Towards Reliable Learning for High Stakes Applications AAAI 2019
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing ACL 2019
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning AISTATS 2019
Calibrating Deep Convolutional Gaussian Processes AISTATS 2019
Information-Theoretic Confidence Bounds for Reinforcement Learning NIPS 2019