Yuling Yao
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Yuling Yao

   
Profile

Yuling Yao


About

I am a sixth-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.

I anticipate to graduate in early 2021.

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.
  • Better inference through a predictive lens: Bayesian procudure is coherent but not automatically optiaml. To what extent should we rely on Bayes posterior; can we make better probabilistic predicion from the model that is not even believed to be true?

I am also interested in applying Bayesian methods to real data problems. Projects that I have been involved inclue replication crisis in psychology, groundwater arsenic in South Asia, soil lead after Notre Dame fire, free energy computation, deep Bayes net on imagenet, and Covid-19 predictions.

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

Published and Submitted Papers  

    Bayesian Methodology

Yuling Yao, Collin Cademartori, Aki Vehtari, Andrew Gelman. [2020]
Adaptive Path Sampling in Metastable Posterior Distributions. preprint.
[preprint] [Package]

Yuling Yao, Aki Vehtari, Andrew Gelman. [2020]
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. under review.
[preprint] [Code]

Andrew Gelman, Yuling Yao. [2020]
Holes in Bayesian Statistics. Journal of Physics G, to appear.
[Online]

Yuling Yao. [2019+]
Bayesian Aggregation. under review.
[preprint]

Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry. [2019+]
Pareto Smoothed Importance Sampling. under review.
[preprint]

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.
[Online]  

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]

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]

    Statistics Applications

Alexander van Geen, Yuling Yao, Tyler Ellis, Andrew Gelman. [2020]
Fallout of Lead over Paris from the 2019 Notre-Dame Cathedral Fire. Geohealth .
[Online]   [Code]   [Media coverage]   [Media coverage 2]

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

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.
[Online]

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

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]

Software

    LOO

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: Sep 2020
© Yuling Yao
  © 2020 Yuling Yao