4 citations · 11 across the 7 of their papers we have counts for
7 papers
Online Learning for Obstacle Avoidance
David Snyder, Meghan Booker, Nathaniel Simon +4
We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations…
Online Nonstochastic Model-Free Reinforcement Learning
Udaya Ghai, Arushi Gupta, Wenhan Xia +2
We investigate robust model-free reinforcement learning algorithms designed for environments that may be dynamic or even adversarial. Traditional state-based policies often struggl…
Adaptive Gradient Methods with Local Guarantees
Zhou Lu, Wenhan Xia, Sanjeev Arora +1
Adaptive gradient methods are the method of choice for optimization in machine learning and used to train the largest deep models. In this paper we study the problem of learning a…
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Daniel Suo, Naman Agarwal, Wenhan Xia +12
Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challe…
Machine Learning for Mechanical Ventilation Control
Daniel Suo, Naman Agarwal, Wenhan Xia +12
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs acco…
Fully Dynamic Inference with Deep Neural Networks
Wenhan Xia, Hongxu Yin, Xiaoliang Dai +1
Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction. Their cross-domain success, however, i…
Efficient Synthesis of Compact Deep Neural Networks
Wenhan Xia, Hongxu Yin, Niraj K. Jha
Deep neural networks (DNNs) have been deployed in myriad machine learning applications. However, advances in their accuracy are often achieved with increasingly complex and deep ne…