@YulingYao

Assistant Professor of Statistics at UT Austin. Research focused on Bayesian statistics, machine learning, and causal inference.

 New York, NY
yulingyao.com
Joined November 2017
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perhaps what can be more shocking than the content of the travel ban itself, is how the order was issued without any resistance, and how the comments under this very thread were filled with groundless hostilities.
Siyuan (Steven) Wang@SStevenWang31 Jan 2025
My Chinese American colleagues in Texas public universities such as the whole U Texas including MD Anderson are now required to report family visit trip to China ahead of trip, and provide a post-travel briefing detailing the trip. Land of the free (your experience may differ).
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We have been using neural posterior/simulation-based inference(SBI) for scientific computing. There was one hole: you run 10 networks for the same task and you obtain 10 different inference. Our paper arxiv.org/abs/2310.17009 attempts to aggregate non-mixing runs of SBI.
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This work combines two of my favorite subjects: SBI and stacking. For the first time, we design a flexible stacking tool that can match not only density, but also posterior confidence intervals, ranks, and moments simultaneously.
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Wasn't sure what Voyages Beyond the SM would achieve beyond the amazing venue & experience, but this was a great week of forced device-disconnection + (unforced) physics discussion. Oh, and sailing across the Aegean! Buzzing with ideas, big thanks to Ezequiel & the slackers ⛵⚛️
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Attending a workshop “Bayesian stats for particle physics beyond the standard model”. Could you be cooler than learning cool physics at Agnes sea?
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apparently there is a second, and arguably better, part of the discussion on Bayes vs overfit statmodeling.stat.columbia.e…
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My controversial opinion: Bayes is guaranteed to overfit, for any model, any prior, and any data point yulingyao.com/blog/2023/over…
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Our new paper on simulation based calibration: arxiv.org/pdf/2305.14593.pdf, likely a better approach to check if your bayesian computing is good, applicable to simulation based inference, vb, or mcmc (no need to thinning™).
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2/3 Traditional simulation based calibration is a goodness-of-fit test: the rank of sample should be uniformly-distributed when drawn from the true density. But rank test, or tail probability, is such a 1940s idea: the era when high dimensional data analysis was not developed
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3/3 Our next-gen SBC uses classification, but it is better than a blackbox classification cuz we can add all statistical information into the classifier. It yields a higher-power test, various divergence estimates and a visualization.
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This week I am attending a "Bayesian statistics in nuclear experiment and theory" workshop in WashU. I expect some mild backlash from physicist after I propose to use quantum superposition to do model averaging.
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3) As it has appeared in many other AI-for-Science fields, the black-box-using-a-GP-to-beat-benchmark-era has gone. The hybrid approach that has built-in domain structure is now overwhelming.
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4) in terms of uncertainty quantification (observation noise, posterior uncertainty, inference error, model error, pde solver error, truncation error), nuclear seems to be one step advance than cosmology, and three steps than causal inference.
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Attending the BFF (Bayesian, Fiducial & Frequentist) meeting this week in cincinnati. Delighted to see many of my favorite nuggets including cross validation, flow, and coarea formula popping up altogether.
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At the Joint Statistical Meeting in DC, I will present at two sessions: Tue, 8/9, 2:00 PM, 308: Highlights in Bayesian Analysis, where I will present hierarchical stacking.  Wed, 8/10, 10:30 AM, 406: panel discussion of Breiman’s Two Cultures. If you are around, please say hi.
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Another example to trick Bayesian inference statmodeling.stat.columbia.e…
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