Xinyi Chen
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Machine learning for medical ventilator control
Cyril Zhang
Daniel Cohen
Edgar Minasyan
Elad Hazan
Julienne LaChance
Karan Singh
Manuel Schottdorf
Paula Nicoleta Gradu
Tom Zajdel
Udaya Ghai
ML4H (2021) (to appear)
Preview abstract
We consider the problem of controlling a medical ventilator for pressure controlled ventilation. The goal is to control airflow in and out of a sedated patient’s lung ac-cording to a trajectory of airway pressures specified by a clinician.
PID, either hand-tuned or using lung-breath simulators based on gas dynamics, is the state-of-the-art control for ventilators.
We consider a data-driven machine learning methodology to tackle this problem via first training a simulator based on collected data and then using this simulator to train controllers based on artificial neural networks. We show that our controller is able to track significantly better than PID controllers on FDA specified benchmarks.
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Efficient Full-Matrix Adaptive Regularization
Naman Agarwal
Brian Anderson Bullins
Karan Singh
Cyril Zhang
Yi Zhang
ICML (2019)
Preview abstract
Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive. We show how to modify full-matrix adaptive regularization in order to make it practical and effective. We also provide novel theoretical analysis for adaptive regularization in non-convex optimization settings. The core of our algorithm, termed GGT, consists of efficient inverse computation of square roots of low-rank matrices. Our preliminary experiments underscore improved convergence rate of GGT across a variety of synthetic tasks and standard deep learning benchmarks.
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