uncertainty quantification

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties NeurIPS 2020 Bootstrapping neural processes NeurIPS 2020 Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors AAAI 2020 Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation AISTATS 2020 Calibrated Reliable Regression using Maximum Mean Discrepancy NeurIPS 2020 Calibrating Structured Output Predictors for Natural Language Processing ACL 2020 Calibration of Pre-trained Transformers EMNLP 2020 Certifiably Adversarially Robust Detection of Out-of-Distribution Data NeurIPS 2020 Classification with Valid and Adaptive Coverage NeurIPS 2020 Correcting Predictions for Approximate Bayesian Inference AAAI 2020 Crowd Counting with Decomposed Uncertainty AAAI 2020 D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry CVPR 2020 Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds ICLR 2020 Deep Evidential Regression NeurIPS 2020 Deep Learning Tubes for Tube MPC RSS 2020 Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization AAAI 2020 Defense Through Diverse Directions ICML 2020 DeltaGrad: Rapid retraining of machine learning models ICML 2020 Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification Interspeech 2020 Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions ICML 2020 Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables ICML 2020 DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs IJCAI 2020 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors ICML 2020 Efficiently sampling functions from Gaussian process posteriors ICML 2020 Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models AAAI 2020