Stephen Ra

According to our database, Stephen Ra authored at least 13 papers between 2019 and 2024.

Collaborative distances:

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

2024

Blind Biological Sequence Denoising with Self-Supervised Set Learning.

Nathan H. Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho

Trans. Mach. Learn. Res., 2024

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design.

Natasa Tagasovska, Ji Won Park, Matthieu Kirchmeyer, Nathan C. Frey, Andrew Martin Watkins, Aya Abdelsalam Ismail, Arian Rokkum Jamasb, Edith Lee, Tyler Bryson, Stephen Ra, Kyunghyun Cho

CoRR, 2024

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks.

Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Concept Bottleneck Generative Models.

Aya Abdelsalam Ismail, Julius Adebayo, Héctor Corrada Bravo, Stephen Ra, Kyunghyun Cho

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Protein Discovery with Discrete Walk-Jump Sampling.

Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hötzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

3D molecule generation by denoising voxel grids.

Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew M. Watkins, Stephen Ra, Vishnu Sresht, Saeed Saremi

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

OpenProteinSet: Training data for structural biology at scale.

Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Daniel Berenberg, Ian Fisk, Andrew M. Watkins, Stephen Ra, Richard Bonneau, Mohammed AlQuraishi

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

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling.

Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences.

Natasa Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijevic

CoRR, 2022

PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design.

Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew M. Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho

CoRR, 2022

Multi-segment preserving sampling for deep manifold sampler.

Daniel Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew M. Watkins, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho

CoRR, 2022

2020

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.

Aaron M. Smith, Jonathan R. Walsh, John Long, Craig B. Davis, Peter Henstock, Martin R. Hodge, Mateusz Maciejewski, Xinmeng Jasmine Mu, Stephen Ra, Shanrong Zhao, Daniel Ziemek, Charles K. Fisher

BMC Bioinform., 2020

2019

Black Box Recursive Translations for Molecular Optimization.

Farhan N. Damani, Vishnu Sresht, Stephen Ra

CoRR, 2019