21 citations · 25 across the 8 of their papers we have counts for
15 papers
Network Clustering for Latent State and Changepoint Detection
Madeline Navarro, Genevera I. Allen, Michael Weylandt
Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be co…
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity
Minjie Wang, Genevera I. Allen
In neuroscience, researchers seek to uncover the connectivity of neurons from large-scale neural recordings or imaging; often people employ graphical model selection and estimation…
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
Kelly Geyer, Frederick Campbell, Andersen Chang +3
ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capab…
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Michael Weylandt, T. Mitchell Roddenberry, Genevera I. Allen
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perh…
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
Mohammad Taha Toghani, Genevera I. Allen
Boosting methods are among the best general-purpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting metho…
Feature Selection for Huge Data via Minipatch Learning
Tianyi Yao, Genevera I. Allen
Feature selection often leads to increased model interpretability, faster computation, and improved model performance by discarding irrelevant or redundant features. While feature…