62 citations · 192 across the 26 of their papers we have counts for
10 papers · 1 filter
The Missing Indicator Method: From Low to High Dimensions
Mike Van Ness, Tomas M. Bosschieter, Roberto Halpin-Gregorio +1
Missing data is common in applied data science, particularly for tabular data sets found in healthcare, social sciences, and natural sciences. Most supervised learning methods only…
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Zachary Frangella, Pratik Rathore, Shipu Zhao +1
SketchySGD improves upon existing stochastic gradient methods in machine learning by using randomized low-rank approximations to the subsampled Hessian and by introducing an automa…
Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data
Yuxuan Zhao, Alex Townsend, Madeleine Udell
Many real-world datasets contain missing entries and mixed data types including categorical and ordered (e.g. continuous and ordinal) variables. Imputing the missing entries is nec…
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles
Brian Liu, Miaolan Xie, Haoyue Yang +1
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this packa…
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams
Iddo Drori, Sarah J. Zhang, Reece Shuttleworth +13
A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large…
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Chengrun Yang, Gabriel Bender, Hanxiao Liu +5
The best neural architecture for a given machine learning problem depends on many factors: not only the complexity and structure of the dataset, but also on resource constraints in…