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Thu 17:00 In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve · Kevin Duarte · Yogesh S Rawat · Mubarak Shah
Thu 17:00 Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen · Ghassen Jerfel · Rafael Müller · Michael W Dusenberry · Jasper Snoek · Balaji Lakshminarayanan · Dustin Tran
Thu 9:00 Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Jake Snell · Richard Zemel
Mon 1:00 Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Cheng Wang · Carolin Lawrence · Mathias Niepert
Tue 9:00 Uncertainty-aware Active Learning for Optimal Bayesian Classifier
Guang Zhao · Edward Dougherty · Byung-Jun Yoon · Francis Alexander · Xiaoning Qian
$\delta$-CLUE: Diverse Sets of Explanations for Uncertainty Estimates
Daniel Ley · Umang Bhatt · Adrian Weller
Wed 17:00 Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
Adam Fisch · Tal Schuster · Tommi Jaakkola · Regina Barzilay
Tue 9:00 Uncertainty Estimation in Autoregressive Structured Prediction
Andrey Malinin · Mark Gales
Thu 9:00 Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
Kanil Patel · William H Beluch · Bin Yang · Michael Pfeiffer · Dan Zhang
Tue 9:00 Getting a CLUE: A Method for Explaining Uncertainty Estimates
Javier Antorán · Umang Bhatt · Tameem Adel · Adrian Weller · José Miguel Hernández Lobato
Wed 9:00 Probabilistic Numeric Convolutional Neural Networks
Marc Finzi · Roberto Bondesan · Max Welling
Mon 9:00 Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Wei Deng · Qi Feng · Georgios Karagiannis · Guang Lin · Faming Liang