1 code implementation • 29 Jun 2023 • Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman
In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where there is uncertainty in both the network topology as well as load and generation variations.
1 code implementation • 29 Jun 2022 • Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability.
1 code implementation • 8 Nov 2021 • Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B. Lobell, Stefano Ermon
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
1 code implementation • 18 Jun 2020 • 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 samples.
3 code implementations • 9 Dec 2019 • 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.
1 code implementation • ICLR 2020 • Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
By removing hidden layers from the target model, using smaller architectures, and training for fewer epochs, we create proxies that are an order of magnitude faster to train.
no code implementations • 10 Nov 2017 • Anthony Perez, Christopher Yeh, George Azzari, Marshall Burke, David Lobell, Stefano Ermon
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce.