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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.

Photo by: Changji Xu

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Department of Statistics,

Room 906 SSW,
1255 Amsterdam Ave,
New York, NY 10027

Published and Submitted Papers  

    Bayesian Statistics

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

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.

"When effect size is tiny and measurement error is huge, you’re essentially trying to use a bathroom scale to weigh a feather —- and the feather is resting loosely in the pouch of a kangaroo that is vigorously jumping up and down."

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, 2016 Asian Political Methodology conference. [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: Dec 2018
© Yuling Yao