Bayesian Inference
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.
Variational inference via Wasserstein gradient flows
Below, we proceed to describe our contributions in greater detail. Our framework builds upon the seminal work of Jordan et al. [1998], which introduced the celebrated
Deep Recurrent Optimal Stopping
Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.
Learning Discrete Latent Variable Structures with Tensor Rank Conditions Zhengming Chen
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures.
