Yuling Yao
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Yuling Yao


Yuling Yao


I am a fourth-year PhD student in Department of Statistics at Columbia University. I am advised by Professor Andrew Gelman. Before coming to Columbia I obtained my undergraduate education from Tsinghua University, where I studied Mathematics.

My general research interest lies in Bayesian statistics and machine learning. My recent research involves:

  • Uncertainty in M-open world: how to do model averaging and model evaluation when the models are wrong, cross validation and marginal likelihood, when these model evaluation methods per se are valid and how to remedy.
  • Reliable inference and computation: how to diagnose variational inference and how to improve, metastability in MCMC sampling algorithms, importance sampling and normalization constant.

I am also interested in applying statistical methods to real data, including replication crisis in psychology, arsenic in groundwater, and penumbra of social network.

I share my random thoughs on statistic and machine learning on my Blog. and other ad-hoc stuff on another Chinese blog .

Published and Submitted Papers  

    Bayesian Statistics

Oscar Chang, Yuling Yao, David Williams-King, Hod Lipson. [2019] Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network. arxiv preprint.
[Online]   [Blog]  

How to approximate posteriors in deep neural networks where even a mean-field gaussian variational inference is prohibitively expensive since it doubles the parameter use?

Aki Vehtari, Daniel Simpson, Yuling Yao, Andrew Gelman [2018] Limitations of "Limitations of Bayesian leave-one-out cross-validation for model selection". Computational Brain & Behavior.

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman [2018] Yes, but did it work?: Evaluating variational inference. Proceedings of the 35th International Conference on Machine Learning.
[Online]   [Blog]   [Code]

"When I say 'I love you', you look accordingly skeptical."

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman [2018] Using stacking to average Bayesian predictive distributions (with discussion and rejoinder). Bayesian Analysis, 13, 917-1003.
[Online]   [Code]   [R package]

"Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian."

    Statistics Applications

Maarten Marsman, Felix D Schönbrodt, Richard D Morey, Yuling Yao, Andrew Gelman, Eric-Jan Wagenmakers [2016] A Bayesian bird's eye view of ‘Replications of important results in social psychology’. Royal Society Open Science,4,160426.

Yu-Sung Su, Yuling Yao [2015] Is the rice dumpling sweet or salty? Adjusting the selection bias of online surveys by multilevel regression and poststratification. (in Chinese) Journal of Tsinghua University,03,43. [Download]

Yu-Sung Su, Yuling Yao [2015] Happy Generations, Depressed Generations: How and Why Chinese People’s Life Satisfactions Vary across Generations, preprint [Download]



(Version 2.0; joint with Gabry J., Vehtari A., and Gelman A.)

R package for efficient approximate leave-one-out cross-validation (LOO) using Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights.
[Source]   [CRAN]

Last Updated: May 2019
© Yuling Yao