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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem



Brian L. Trippe , Jason Yim , Doug Tischer , Tamara Broderick , David Baker , Regina Barzilay , Tommi Jaakkola


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Many processors, little time: MCMC for partitions via optimal transport couplings



Tin D. Nguyen , Brian L. Trippe , Tamara Broderick

* Appearing in AISTATS 2022 

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Toward a Taxonomy of Trust for Probabilistic Machine Learning



Tamara Broderick , Andrew Gelman , Rachael Meager , Anna L. Smith , Tian Zheng

* 18 pages, 2 figures 

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Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression



William T. Stephenson , Zachary Frangella , Madeleine Udell , Tamara Broderick

* 20 pages, 6 figures 

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For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets



Brian L. Trippe , Hilary K. Finucane , Tamara Broderick

* 10 pages plus supplementary material 

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Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics



Ryan Giordano , Runjing Liu , Michael I. Jordan , Tamara Broderick

* 65 pages, 20 figures 

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The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time



Raj Agrawal , Tamara Broderick


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Measuring the sensitivity of Gaussian processes to kernel choice



William T. Stephenson , Soumya Ghosh , Tin D. Nguyen , Mikhail Yurochkin , Sameer K. Deshpande , Tamara Broderick


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Independent finite approximations for Bayesian nonparametric inference: construction, error bounds, and practical implications



Tin D. Nguyen , Jonathan Huggins , Lorenzo Masoero , Lester Mackey , Tamara Broderick


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