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Found 5 papers, 3 papers with code
Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts
The key idea is the design of switching policies that can take conformal quantiles as input, which we define as conformal policy learning, that allows robots to detect distribution shifts with formal statistical guarantees.
Learned, Uncertainty-driven Adaptive Acquisition for Photon-Efficient Multiphoton Microscopy
Finally, with our adaptive acquisition technique, we demonstrate a 120X reduction in acquisition time and total light dose while successfully recovering fine features in the sample.
Optimal Data Selection: An Online Distributed View
The blessing of ubiquitous data also comes with a curse: the communication, storage, and labeling of massive, mostly redundant datasets.
Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making.
Uncertainty Sets for Image Classifiers using Conformal Prediction
Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings.
