Brian L. Trippe

According to our database, Brian L. Trippe authored at least 8 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models.

Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem.

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

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

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

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

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2019

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations.

Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions.

Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019