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cs.LG2026
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
Kun Jin, James Harrison, Jiawei Li +8
Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the c…
cs.LG2024★ 1 cited
Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts
Zichang Liu, Qingyun Liu, Yuening Li +6
Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality. For example, in our experimen…
cs.LG2024
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui +8
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various task…