11 citations · 16 across the 4 of their papers we have counts for
13 papers
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies
Haohan Wang, Bryon Aragam, Eric Xing
Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) a…
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Ben Lengerich, Caleb Ellington, Bryon Aragam +2
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity…
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Goutham Rajendran, Bohdan Kivva, Ming Gao +1
Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning direc…
A polynomial-time algorithm for learning nonparametric causal graphs
Ming Gao, Yi Ding, Bryon Aragam
We establish finite-sample guarantees for a polynomial-time algorithm for learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is mode…
DYNOTEARS: Structure Learning from Time-Series Data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai +4
We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slic…
Automated Dependence Plots
David I. Inouye, Liu Leqi, Joon Sik Kim +2
In practical applications of machine learning, it is necessary to look beyond standard metrics such as test accuracy in order to validate various qualitative properties of a model.…