Publications

Luhuan Wu, Brian L Trippe, Christian A Naesseth, David M Blei, John P Cunningham (2023). Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. Neural Information Processing Systems.

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Elle M Weeks, Jacob C Ulirsch, Nathan Y Cheng, Brian L Trippe, Rebecca S Fine, Jenkai Miao, Tejal A Patwardhan, Masahiro Kanai, Joseph Nasser, Charles P Fulco, Katherine C. Tashman, Francois Aguet, Taibo Li, Jose Ordovas-Montanes, Christopher S Smillie, Moshe Biton, Alex K Shalek, Ashwin N Ananthakrishnan, Ramnik J Xavier, Aviv Regev, Rajat M Gupta, Kasper Lage, Kristin G Ardlie, Joel N Hirschhorn, Eric S Lander, Jesse M Engreitz, Hilary K Finucane (2023). Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nature Genetics.

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Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, Basile I M Wicky, Nikita Hanikel, Samuel J Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S Jaakkola, Frank DiMaio, Minkyung Baek, David Baker (2023). De novo design of protein structure and function with RFdiffusion. Nature.

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Renato Berlinghieri, Brian L Trippe, David R Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick (2023). Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents. International Conference on Machine Learning.

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Brian L Trippe, Sameer K Deshpande, Tamara Broderick (2023). Confidently Comparing Estimates with the c-value. The Journal of the American Statistical Association.

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Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S Jaakkola (2023). SE(3) diffusion model with application to protein backbone generation. International Conference on Machine Learning.

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Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, Tommi Jaakkola (2022). Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. International Conference on Learning Representations.

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Brian L Trippe, Buwei Huang, Erika A DeBenedictis, Brian Coventry, Nicholas Bhattacharya, Kevin K Yang, David Baker, Lorin Crawford (2022). Randomized gates eliminate bias in sort-seq assays. Protein Science.

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Tin D Nguyen, Brian L Trippe, Tamara Broderick (2022). Many processors, little time: MCMC for partitions via optimal transport couplings. International Conference on Artificial Intelligence and Statistics.

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Brian L Trippe, Hilary K Finucane, Tamara Broderick (2021). For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. Neural Information Processing Systems.

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Brian L Trippe, Tin D Nguyen, Tamara Broderick (2021). Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation. arXiv preprint arXiv:2104.04514 Third Symposium on Advances in Approximate Bayesian Inference. 2021.

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Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick (2019). The kernel interaction trick: Fast bayesian discovery of pairwise interactions in high dimensions. International Conference on Machine Learning.

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Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick (2019). LR-GLM: High-dimensional Bayesian inference using low-rank data approximations. International Conference on Machine Learning.

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Brian Trippe, Richard Turner (2018). Overpruning in variational bayesian neural networks. arXiv preprint arXiv:1801.06230.

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Brian L Trippe, Richard E Turner (2018). Conditional density estimation with bayesian normalising flows. arXiv preprint arXiv:1802.04908.

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