1 code implementation • 14 Jun 2021 • Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar
We propose a residual randomization procedure designed for robust Lasso-based inference in the high-dimensional setting.
1 code implementation • 6 May 2021 • Boxin Zhao, Percy S. Zhai, Y. Samuel Wang, Mladen Kolar
We propose a neighborhood selection approach to estimate the structure of Gaussian functional graphical models, where we first estimate the neighborhood of each node via a function-on-function regression and subsequently recover the entire graph structure by combining the estimated neighborhoods.
no code implementations • 11 Mar 2020 • Boxin Zhao, Y. Samuel Wang, Mladen Kolar
We first define a functional differential graph that captures the differences between two functional graphical models and formally characterize when the functional differential graph is well defined.
1 code implementation • NeurIPS 2019 • Boxin Zhao, Y. Samuel Wang, Mladen Kolar
We consider the problem of estimating the difference between two functional undirected graphical models with shared structures.
2 code implementations • 9 Jul 2018 • Wenyu Chen, Mathias Drton, Y. Samuel Wang
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances.
Methodology Computation
no code implementations • 29 Nov 2017 • Yen-Chi Chen, Y. Samuel Wang, Elena A. Erosheva
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning.
1 code implementation • 29 Dec 2015 • Y. Samuel Wang, Ross Matsueda, Elena A. Erosheva
In this article, we consider modeling ranked responses from a heterogeneous population.
Methodology Applications