Search Results for author: Brian L. Trippe

Found 7 papers, 4 papers with code

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents

1 code implementation20 Feb 2023 Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick

Given sparse observations of buoy velocities, oceanographers are interested in reconstructing ocean currents away from the buoys and identifying divergences in a current vector field.

Gaussian Processes

SE(3) diffusion model with application to protein backbone generation

1 code implementation5 Feb 2023 Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi Jaakkola

The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry.

Protein Structure Prediction

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

1 code implementation8 Jun 2022 Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi Jaakkola

Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes.

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

1 code implementation23 Feb 2022 Tin D. Nguyen, Brian L. Trippe, Tamara Broderick

In MCMC samplers of continuous random variables, Markov chain couplings can overcome bias.

Clustering

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

no code implementations NeurIPS 2021 Brian L. Trippe, Hilary K. Finucane, Tamara Broderick

While standard practice is to model regression parameters (effects) as (1) exchangeable across datasets and (2) correlated to differing degrees across covariates, we show that this approach exhibits poor statistical performance when the number of covariates exceeds the number of datasets.

regression

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

no code implementations17 May 2019 Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome.

Bayesian Inference Vocal Bursts Intensity Prediction

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