32 citations · 38 across the 4 of their papers we have counts for
5 papers
Assumption-Lean Inference for Spectral Differential Network Analysis of High-Dimensional Time Series
Michael Hellstern, Byol Kim, Ali Shojaie
Network analysis for multivariate time series is popular in many fields, from neuroscience to seismology. The inverse spectral density is a common choice for time series network an…
Spectral Differential Network Analysis for High-Dimensional Time Series
Michael Hellstern, Byol Kim, Zaid Harchaoui +1
Spectral networks derived from multivariate time series data arise in many domains, from brain science to Earth science. Often, it is of interest to study how these networks change…
Black-box tests for algorithmic stability
Byol Kim, Rina Foygel Barber
Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g., removal of a single data point) may affect the outputs o…
Predictive Inference Is Free with the Jackknife+-after-Bootstrap
Byol Kim, Chen Xu, Rina Foygel Barber
Ensemble learning is widely used in applications to make predictions in complex decision problems---for example, averaging models fitted to a sequence of samples bootstrapped from…
Two-sample inference for high-dimensional Markov networks
Byol Kim, Song Liu, Mladen Kolar
Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of inte…