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2020 – today
- 2023
[c8]Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola:
Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. ICLR 2023- [c7]Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan
, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. ICML 2023: 2113-2163 - [c6]Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola:
SE(3) diffusion model with application to protein backbone generation. ICML 2023: 40001-40039 - [c5]Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham:
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. NeurIPS 2023 - [i7]Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola:
SE(3) diffusion model with application to protein backbone generation. CoRR abs/2302.02277 (2023) - [i6]Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick:
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. CoRR abs/2302.10364 (2023) - [i5]Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham:
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. CoRR abs/2306.17775 (2023) - 2022
- [c4]Tin D. Nguyen, Brian L. Trippe, Tamara Broderick:
Many processors, little time: MCMC for partitions via optimal transport couplings. AISTATS 2022: 3483-3514 - [i4]Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola:
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. CoRR abs/2206.04119 (2022) - 2021
- [c3]Brian L. Trippe, Hilary K. Finucane, Tamara Broderick:
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. NeurIPS 2021: 13471-13484 - [i3]Brian L. Trippe, Hilary K. Finucane, Tamara Broderick:
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. CoRR abs/2107.06428 (2021)
2010 – 2019
- 2019
- [c2]Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML 2019: 141-150 - [c1]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML 2019: 6315-6324 - [i2]Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR abs/1905.06501 (2019) - [i1]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR abs/1905.07499 (2019)
Coauthor Index
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