Search papers, labs, and topics across Lattice.
D environment, where our method eclipses the peak performance of RL-BOED and DAD with 10
3
0
4
POLAR achieves superior performance in adaptive data acquisition by harnessing pretrained models, requiring dramatically fewer training samples than existing methods.
FairBED shows that you can design data acquisition processes that inherently reduce bias, leading to fairer machine learning models.
Task-driven Bayesian experimental design just got a major upgrade, enabling efficient learning of design policies without the complexity of posterior estimation.
