6 papers · 1 filter
Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians
William N. Caballero, Matthew LaRosa, Alexander Fisher +1
The multivariate Gaussian distribution underpins myriad operations-research, decision-analytic, and machine-learning models (e.g., Bayesian optimization, Gaussian influence diagram…
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation
Qi Le, Enmao Diao, Xinran Wang +3
Federated Learning (FL) is a collaborative machine learning framework that allows multiple users to train models utilizing their local data in a distributed manner. However, consid…
Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions
Patrick Kuiper, Ali Hasan, Wenhao Yang +4
The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports us…
Generative Learning for Simulation of Vehicle Faults
Patrick Kuiper, Sirui Lin, Jose Blanchet +1
We develop a novel generative model to simulate vehicle health and forecast faults, conditioned on practical operational considerations. The model, trained on data from the US Army…
Base Models for Parabolic Partial Differential Equations
Xingzi Xu, Ali Hasan, Jie Ding +1
Parabolic partial differential equations (PDEs) appear in many disciplines to model the evolution of various mathematical objects, such as probability flows, value functions in con…
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes
Haoming Yang, Ali Hasan, Yuting Ng +1
McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical description of the behavior of an infinite number of interacting particles by imposing a dependence…