Paper-Conference

Fine-grained uncertainty decomposition in large language models: A spectral approach
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LVLM-Aided Alignment of Task-Specific Vision Models
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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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Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
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MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
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Provably Better Explanations with Optimized Aggregation of Feature Attributions
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