21 citations · 25 across the 11 of their papers we have counts for
8 papers · 1 filter
Data Augmentation via Subgroup Mixup for Improving Fairness
Madeline Navarro, Camille Little, Genevera I. Allen +1
In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine learning systems exhibit biases ac…
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities
Genevera I. Allen, Luqin Gan, Lili Zheng
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only p…
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…
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…