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Found 6 papers, 3 papers with code
Generative Modeling for Tabular Data via Penalized Optimal Transport Network
To this end, we propose POTNet (Penalized Optimal Transport Network), a generative deep neural network based on a novel, robust, and interpretable marginally-penalized Wasserstein (MPW) loss.
CausalEGM: a general causal inference framework by encoding generative modeling
In this article, we develop a general framework $\textit{CausalEGM}$ for estimating causal effects by encoding generative modeling, which can be applied in both binary and continuous treatment settings.
Roundtrip: A Deep Generative Neural Density Estimator
Density estimation is a fundamental problem in both statistics and machine learning.
Convergence rates of a partition based Bayesian multivariate density estimation method
We study a class of non-parametric density estimators under Bayesian settings.
Density Estimation via Discrepancy Based Adaptive Sequential Partition
Given $iid$ observations from an unknown absolute continuous distribution defined on some domain $\Omega$, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function.
Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach
Model-based methods founded on quantitative descriptions of gene regulation are among the most promising, but many such methods rely on simple, local models or on ad hoc inference approaches lacking experimental interpretability.
