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Found 12 papers, 3 papers with code
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes
We empirically compare the performance of the different architectures and estimators on real and synthetic datasets for time series and probabilistic modeling.
PrACTiS: Perceiver-Attentional Copulas for Time Series
Transformers incorporating copula structures have demonstrated remarkable performance in time series prediction.
Individual Treatment Effects in Extreme Regimes
Understanding individual treatment effects in extreme regimes is important for characterizing risks associated with different interventions.
Inference and Sampling of Point Processes from Diffusion Excursions
In this framework, we relate the return times of a diffusion in a continuous path space to new arrivals of the point process.
Inference and Sampling for Archimax Copulas
Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects.
Characteristic Neural Ordinary Differential Equations
While NODEs model the evolution of a latent variables as the solution to an ODE, C-NODE models the evolution of the latent variables as the solution of a family of first-order quasi-linear partial differential equations (PDEs) along curves on which the PDEs reduce to ODEs, referred to as characteristic curves.
Generative Archimedean Copulas
We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas.
Modeling Extremes with d-max-decreasing Neural Networks
We propose a novel neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs).
Identifying Latent Stochastic Differential Equations
We present a method for learning latent stochastic differential equations (SDEs) from high-dimensional time series data.
Fisher Auto-Encoders
It has been conjectured that the Fisher divergence is more robust to model uncertainty than the conventional Kullback-Leibler (KL) divergence.
Learning Partial Differential Equations from Data Using Neural Networks
We develop a framework for estimating unknown partial differential equations from noisy data, using a deep learning approach.
Image-based immersed boundary model of the aortic root
Each year, approximately 300, 000 heart valve repair or replacement procedures are performed worldwide, including approximately 70, 000 aortic valve replacement surgeries in the United States alone.
