Search Results for author:
Found 5 papers, 0 papers with code
Distributionally Robust Active Learning for Gaussian Process Regression
We show an upper bound of the worst-case expected squared error, which suggests that the error will be arbitrarily small by a finite number of data labels under mild conditions.
Distributionally Robust Coreset Selection under Covariate Shift
DRCS theoretically derives an estimate of the upper bound for the worst-case test error, assuming that the future covariate distribution may deviate within a defined range from the training distribution.
Distributionally Robust Safe Sample Elimination under Covariate Shift
We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions.
Integrated Assessment Modeling of Korea 2050 Carbon Neutrality Technology Pathways
This integrated assessment modeling research analyzes what Korea's 2050 carbon neutrality would require for the national energy system and the role of the power sector concerning the availability of critical mitigation technologies.
$f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception
While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as self-driving vehicles.
