De novo design of protein structure and function with RFdiffusion von Watson, Joseph L., Juergens, David, Bennett, Nathaniel R., Trippe, Brian L., Yim, Jason, Eisenach, Helen E., Ahern, Woody, Borst, Andrew J., Ragotte, Robert J., Milles, Lukas F., Wicky, Basile I. M., Hanikel, Nikita, Pellock, Samuel J., Courbet, Alexis, Sheffler, William, Wang, Jue, Venkatesh, Preetham, Sappington, Isaac, Torres, Susana Vázquez, Lauko, Anna, De Bortoli, Valentin, Mathieu, Emile, Ovchinnikov, Sergey, Barzilay, Regina, Jaakkola, Tommi S., DiMaio, Frank, Baek, Minkyung, Baker, David

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Confidently Comparing Estimates with the c-value von Trippe, Brian L., Deshpande, Sameer K., Broderick, Tamara

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Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases von Weeks, Elle M., Ulirsch, Jacob C., Cheng, Nathan Y., Trippe, Brian L., Fine, Rebecca S., Miao, Jenkai, Patwardhan, Tejal A., Kanai, Masahiro, Nasser, Joseph, Fulco, Charles P., Tashman, Katherine C., Aguet, Francois, Li, Taibo, Ordovas-Montanes, Jose, Smillie, Christopher S., Biton, Moshe, Shalek, Alex K., Ananthakrishnan, Ashwin N., Xavier, Ramnik J., Regev, Aviv, Gupta, Rajat M., Lage, Kasper, Ardlie, Kristin G., Hirschhorn, Joel N., Lander, Eric S., Engreitz, Jesse M., Finucane, Hilary K.

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Randomized gates eliminate bias in sort‐seq assays von Trippe, Brian L., Huang, Buwei, DeBenedictis, Erika A., Coventry, Brian, Bhattacharya, Nicholas, Yang, Kevin K., Baker, David, Crawford, Lorin

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Inhibition of cell fate repressors secures the differentiation of the touch receptor neurons of Caenorhabditis elegans von Zheng, Chaogu, Jin, Felix Qiaochu, Trippe, Brian Loeber, Wu, Ji, Chalfie, Martin

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Using mobile sequencers in an academic classroom von Zaaijer, Sophie, Erlich, Yaniv

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Overpruning in Variational Bayesian Neural Networks von Trippe, Brian, Turner, Richard

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Many processors, little time: MCMC for partitions via optimal transport couplings von Nguyen, Tin D, Trippe, Brian L, Broderick, Tamara

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Practical and Asymptotically Exact Conditional Sampling in Diffusion Models von Wu, Luhuan, Trippe, Brian L, Naesseth, Christian A, Blei, David M, Cunningham, John P

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For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets von Trippe, Brian L, Finucane, Hilary K, Broderick, Tamara

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SE(3) diffusion model with application to protein backbone generation von Yim, Jason, Trippe, Brian L, De Bortoli, Valentin, Mathieu, Emile, Doucet, Arnaud, Barzilay, Regina, Jaakkola, Tommi

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Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation von Trippe, Brian L, Nguyen, Tin D, Broderick, Tamara

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Confidently Comparing Estimators with the c-value von Trippe, Brian L, Deshpande, Sameer K, Broderick, Tamara

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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem von Trippe, Brian L, Yim, Jason, Tischer, Doug, Baker, David, Broderick, Tamara, Barzilay, Regina, Jaakkola, Tommi

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Gaussian processes at the Helm(holtz): A more fluid model for ocean currents von Berlinghieri, Renato, Trippe, Brian L, Burt, David R, Giordano, Ryan, Srinivasan, Kaushik, Özgökmen, Tamay, Xia, Junfei, Broderick, Tamara

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Conditional Density Estimation with Bayesian Normalising Flows von Trippe, Brian L, Turner, Richard E

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LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations von Trippe, Brian L, Huggins, Jonathan H, Agrawal, Raj, Broderick, Tamara

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The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions von Agrawal, Raj, Huggins, Jonathan H, Trippe, Brian, Broderick, Tamara

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Practical and Asymptotically Exact Conditional Sampling in Diffusion Models von Wu, Luhuan, Trippe, Brian L, Naesseth, Christian A, Blei, David M, Cunningham, John P

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Overpruning in Variational Bayesian Neural Networks von Trippe, Brian, Turner, Richard

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