no code implementations • 15 Apr 2024 • Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh
We empirically compare the performance of the different architectures and estimators on real and synthetic datasets for time series and probabilistic modeling.
no code implementations • 3 Oct 2023 • Cat P. Le, Chris Cannella, Ali Hasan, Yuting Ng, Vahid Tarokh
Transformers incorporating copula structures have demonstrated remarkable performance in time series prediction.
no code implementations • 20 Jun 2023 • Ahmed Aloui, Ali Hasan, Yuting Ng, Miroslav Pajic, Vahid Tarokh
Understanding individual treatment effects in extreme regimes is important for characterizing risks associated with different interventions.
no code implementations • 1 Jun 2023 • Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh
In this framework, we relate the return times of a diffusion in a continuous path space to new arrivals of the point process.
no code implementations • 27 May 2022 • Yuting Ng, Ali Hasan, Vahid Tarokh
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.
no code implementations • 25 Nov 2021 • Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh
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.
1 code implementation • 22 Feb 2021 • Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh
We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas.
no code implementations • 17 Feb 2021 • Ali Hasan, Khalil Elkhalil, Yuting Ng, Joao M. Pereira, Sina Farsiu, Jose H. Blanchet, Vahid Tarokh
We propose a novel neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs).
1 code implementation • 12 Jul 2020 • Ali Hasan, João M. Pereira, Sina Farsiu, Vahid Tarokh
We present a method for learning latent stochastic differential equations (SDEs) from high-dimensional time series data.
no code implementations • 12 Jul 2020 • Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh
It has been conjectured that the Fisher divergence is more robust to model uncertainty than the conventional Kullback-Leibler (KL) divergence.
1 code implementation • 22 Oct 2019 • Ali Hasan, João M. Pereira, Robert Ravier, Sina Farsiu, Vahid Tarokh
We develop a framework for estimating unknown partial differential equations from noisy data, using a deep learning approach.
no code implementations • 4 May 2017 • Ali Hasan, Ebrahim M. Kolahdouz, Andinet Enquobahrie, Thomas G. Caranasos, John P. Vavalle, Boyce E. Griffith
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.