Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...
aInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA bSchool of Medicine, University of Washington, Seattle, WA, USA cGeneral Medicine Service, Department of ...
Abstract: This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant (LTI) systems under additive stochastic disturbances. It first constructs a ...
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
By: Ahmed Awadallah, Sahil Gupta, Yash Lara, Yadong Lu, Hussein Mozannar, Akshay Nambi, Zach Nussbaum, Yash Pandya, Aravind Rajeswaran, Corby Rosset, Alexey Taymanov, Luiz do Valle, Vibhav Vineet, ...
Abstract: Training machine learning models often involves solving high-dimensional stochastic optimization problems, where stochastic gradient-based algorithms are hindered by slow convergence.
Single-cell RNA sequencing has revolutionized our ability to dissect cellular heterogeneity and study cell fate mechanisms, yet inferring stochastic dynamics from static snapshots remains a ...
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