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Uncertainty Quantification

663 directly classified papers

Papers per year

Papers

Uncertainty-Aware Deep Classifiers Using Generative Models AAAI 2020
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents AAAI 2020
Justification-Based Reliability in Machine Learning AAAI 2020
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics AAAI 2020
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization AAAI 2020
Towards More Accurate Uncertainty Estimation In Text Classification EMNLP 2020
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation EMNLP 2020
Probabilistic Object Detection: Definition and Evaluation WACV 2020
Uncertainty Estimation Using a Single Deep Deterministic Neural Network ICML 2020
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification JMLR 2020
Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications JMLR 2020
Posterior Calibrated Training on Sentence Classification Tasks ACL 2020
On the Inference Calibration of Neural Machine Translation ACL 2020
Uncertain Natural Language Inference ACL 2020
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation ACL 2020
Bayesian Graph Neural Networks with Adaptive Connection Sampling ICML 2020
Parametric Gaussian Process Regressors ICML 2020
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation ICML 2020
Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020
Training Binary Neural Networks using the Bayesian Learning Rule ICML 2020
Confidence-Aware Learning for Deep Neural Networks ICML 2020
Detecting Out-of-Distribution Examples with Gram Matrices ICML 2020
Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract) AAAI 2020
Temporal Logics Over Finite Traces with Uncertainty AAAI 2020
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors AAAI 2020