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Found 8 papers, 1 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.
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).
Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity.
