Paper-Conference

FedAgree: Label-Free Performance Estimation under Distribution Shift for Federated Medical Analysis
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Fine-grained uncertainty decomposition in large language models: A spectral approach
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From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
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LVLM-Aided Alignment of Task-Specific Vision Models
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Non-Contrastive Vision-Language Learning with Predictive Embedding Alignment
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Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
We study the problem of monitoring machine learning models under gradual distribution shifts, where circumstances change slowly over …
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Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers
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Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond
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A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
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Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
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