1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
David R. Burt, Renato Berlinghieri, Tamara Broderick
Scientists are often interested in estimating an association between a covariate and a binary- or count-valued response. For instance, public health officials are interested in how…
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Jenny Y. Huang, Yunyi Shen, Dennis Wei +1
We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a worst-case very small fraction of prefer…
Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schrödinger Bridge's End
Renato Berlinghieri, Yunyi Shen, Jialong Jiang +1
Scientists often want to make predictions beyond the observed time horizon of "snapshot" data following latent stochastic dynamics. For example, in time course single-cell mRNA pro…
Wild posteriors in the wild
Yunyi Shen, Tamara Broderick
Bayesian posterior approximation has become more accessible to practitioners than ever, thanks to modern black-box software. While these tools provide highly accurate approximation…
Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
David R. Burt, Renato Berlinghieri, Stephen Bates +1
Estimating associations between spatial covariates and responses - rather than merely predicting responses - is central to environmental science, epidemiology, and economics. For i…