The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. - Raj Agrawal; Brian L. Trippe; Jonathan H. Huggins; Tamara Broderick (ICML, 2019)

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. - Brian L. Trippe; Jonathan H. Huggins; Raj Agrawal; Tamara Broderick (ICML, 2019)

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

Many processors, little time: MCMC for partitions via optimal transport couplings. - Tin D. Nguyen; Brian L. Trippe; Tamara Broderick (AISTATS, 2022)

Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. - Brian L. Trippe; Jason Yim; Doug Tischer; David Baker 001; Tamara Broderick; Regina Barzilay; Tommi S. Jaakkola (ICLR, 2023)

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. - Renato Berlinghieri; Brian L. Trippe; David R. Burt; Ryan James Giordano; Kaushik Srinivasan; Tamay M. Özgökmen; Junfei Xia; Tamara Broderick (ICML, 2023)

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

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

MotifBench: A standardized protein design benchmark for motif-scaffolding problems. - Zhuoqi Zheng; Bo Zhang; Kieran Didi; Kevin K. Yang; Jason Yim; Joseph L. Watson; Hai-Feng Chen; Brian L. Trippe (CoRR, 2025)

Predicting mutational effects on protein binding from folding energy. - Arthur Deng; Karsten Householder; Fang Wu; Sebastian Thrun; K. Christopher Garcia; Brian L. Trippe (CoRR, 2025)