4 papers · 1 filter
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…
Bayesian Optimization via Continual Variational Last Layer Training
Paul Brunzema, Mikkel Jordahn, John Willes +3
Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on…
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Avi Singh, John D. Co-Reyes, Rishabh Agarwal +38
Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of hi…
Variational Bayesian Last Layers
James Harrison, John Willes, Jasper Snoek
We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively impr…