Learning with Uncertain Labels

Learning Auto-Structured Regressor from Uncertain Nonnegative LabelsShuicheng Yan, Huan Wang, Xiaoou Tang, Thomas S. Huang Mathematical Formulation Learning with Uncertain Labels Evaluation Criteria Experiment Results Uncertainty Effectiveness Estimated pose labels of the three images in Pointing04 from 13different observers by rotating a 3D head model. We can see thatlarge standard deviations exist for these labeled ground truths. Algorithm Convergence Label is Uncertain and Nonnegative !!! Makeup greatly affects observed age Living condition affects observed age An integer age l means the age within [l, l+1) Without ground truth, the age estimation is subject-dependent Iterative Procedure Flowchart Age Estimation Pose Estimation

Learning with Uncertain Labels

Learning with Uncertain Labels

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Scheduling with Uncertain Resources

Labels

Uncertain Standards with Standard Uncertainties

Learning About Food Labels

Uncertain

Labels

Cleaning Uncertain Data with Quality Guarantees

On Multiple Kernel Learning with Multiple Labels

COPING WITH UNCERTAIN TIMES

Cleaning Uncertain Data with Quality Guarantees

International Environmental Agreements with Uncertain Environmental Damage and Learning

Narrative Authoring with Uncertain Time Inference

Probabilistic Temporal Planning with Uncertain Durations

Ranking with Uncertain Scores

Probabilistic Reasoning with Uncertain Data

Clothes Labels- Easy Labels

Cosmetic Labels, Paper Labels

Probabilistic Reasoning with Uncertain Data

Uncertain Multiagent Systems: Games and Learning

Apparel with Woven Labels