91 citations · 168 across the 8 of their papers we have counts for
8 papers
End-to-End Conformal Calibration for Optimization Under Uncertainty
Christopher Yeh, Nicolas Christianson, Alan Wu +2
Machine learning can significantly improve performance for decision-making under uncertainty across a wide range of domains. However, ensuring robustness guarantees requires well-c…
Online learning for robust voltage control under uncertain grid topology
Christopher Yeh, Jing Yu, Yuanyuan Shi +1
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is challenging…
Robust Online Voltage Control with an Unknown Grid Topology
Christopher Yeh, Jing Yu, Yuanyuan Shi +1
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is a challengin…
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Christopher Yeh, Chenlin Meng, Sherrie Wang +7
Progress toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically ha…
A Framework for Sample Efficient Interval Estimation with Control Variates
Shengjia Zhao, Christopher Yeh, Stefano Ermon
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samp…
Efficient Object Detection in Large Images using Deep Reinforcement Learning
Burak Uzkent, Christopher Yeh, Stefano Ermon
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images. However, in so…