10,508 research outputs found
Supporting Regularized Logistic Regression Privately and Efficiently
As one of the most popular statistical and machine learning models, logistic
regression with regularization has found wide adoption in biomedicine, social
sciences, information technology, and so on. These domains often involve data
of human subjects that are contingent upon strict privacy regulations.
Increasing concerns over data privacy make it more and more difficult to
coordinate and conduct large-scale collaborative studies, which typically rely
on cross-institution data sharing and joint analysis. Our work here focuses on
safeguarding regularized logistic regression, a widely-used machine learning
model in various disciplines while at the same time has not been investigated
from a data security and privacy perspective. We consider a common use scenario
of multi-institution collaborative studies, such as in the form of research
consortia or networks as widely seen in genetics, epidemiology, social
sciences, etc. To make our privacy-enhancing solution practical, we demonstrate
a non-conventional and computationally efficient method leveraging distributing
computing and strong cryptography to provide comprehensive protection over
individual-level and summary data. Extensive empirical evaluation on several
studies validated the privacy guarantees, efficiency and scalability of our
proposal. We also discuss the practical implications of our solution for
large-scale studies and applications from various disciplines, including
genetic and biomedical studies, smart grid, network analysis, etc
Transferable atomic multipole machine learning models for small organic molecules
Accurate representation of the molecular electrostatic potential, which is
often expanded in distributed multipole moments, is crucial for an efficient
evaluation of intermolecular interactions. Here we introduce a machine learning
model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any
molecular conformation. The model is trained on quantum chemical results for
atoms in varying chemical environments drawn from thousands of organic
molecules. Multipoles in systems with neutral, cationic, and anionic molecular
charge states are treated with individual models. The models' predictive
accuracy and applicability are illustrated by evaluating intermolecular
interaction energies of nearly 1,000 dimers and the cohesive energy of the
benzene crystal.Comment: 11 pages, 6 figure
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