In recent years, the ML community has seen surges of interest in both adversarially robust
learning and implicit layers, but connections between these two areas have seldom been …

Locational marginal emissions rates (LMEs) estimate the rate of change in emissions due to
a small change in demand in a transmission network, and are an important metric for …

In recent years, data disclosure has become a global trend. While making public data of
power systems available facilitated research and development efforts, it also brought more …

Modern power systems are increasingly vulnerable to stealthy false data injection attacks
(FDIAs) targeting state estimation. Executed without the operator's knowledge, these attacks …

This work presents developments in differentiable optimization, with applications to the
computation of marginal emissions in power system models. First, we discuss how recent …

Climate change is one of the most pressing issues of our time, requiring the rapid
mobilization of many tools and approaches from across society. Machine learning has been …

Locational marginal emissions rates (LMEs) estimate the rate of change in emissions due to
a small change in demand in a transmission network, and are an important metric for …