John Scalzi is a voluble man. He is the author of several New York Times best sellers and has been nominated for nearly every major award that the science-fiction industry has to offer--some of which he's won multiple times. Over the course of his career, he has written millions of words, filling dozens of books and 27 years' worth of posts on his personal blog. All of this is to say that if one wants to cite Scalzi, there is no shortage of material. But this month, the author noticed something odd: He was being quoted as saying things he'd never said.

In particular, we propose an adaptive evaluation method that statistically early terminates a costly hardware measurement without losing much accuracy. We further devise a surrogate model with uncertainty quantification that allows the optimization to adapt to hardware and model heterogeneity better.

Bayesian inference for the truncated GP can be achieved, in many cases with substantially lower computation than would be required using MCMC methods.

V is constrained to vary along a limited set of dimensions.

Several additional assumptions were suggested to make the ICA problem identifiable.

Identifying interpretable patterns in multi-electrode recordings is a longstanding and increasingly pressing challenge in neuroscience.

One major concern of AM is the heavier memory overhead compared with the fixed-point iteration.