5 papers
ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Gabe Guo, Thanawat Sornwanee, Lutong Hao +3
Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., first and last frames) is a fundament…
Score-based Metropolis-Hastings for Fractional Langevin Algorithms
Ahmed Aloui, Junyi Liao, Ali Hasan +2
Sampling from heavy-tailed and multimodal distributions is challenging when neither the target density nor the proposal density can be evaluated, as in -stable Lévy-driven fract…
Limit Theorems for Stochastic Gradient Descent with Infinite Variance
Jose Blanchet, Aleksandar Mijatović, Wenhao Yang
Stochastic gradient descent is a classic algorithm that has gained great popularity especially in the last decades as the most common approach for training models in machine learni…
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…