18 citations · 68 across the 14 of their papers we have counts for
4 papers · 1 filter
PAC Confidence Predictions for Deep Neural Network Classifiers
Sangdon Park, Shuo Li, Insup Lee +1
A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose…
Joint Orthogonal Band and Power Allocation for Energy Fairness in WPT System with Nonlinear Logarithmic Energy Harvesting Model
Jaeseob Han, Gyeong Ho Lee, Sangdon Park +1
Wireless power transmission (WPT) is expected to play an important role in the Internet of Things services by providing the perpetual operation of IoT sensors. However, to prolong…
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Sangdon Park, Osbert Bastani, James Weimer +1
Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches…
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
Sangdon Park, Osbert Bastani, Nikolai Matni +1
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i…