Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. - Marc Finzi; Samuel Stanton; Pavel Izmailov; Andrew Gordon Wilson (ICML, 2020)

Kernel Interpolation for Scalable Online Gaussian Processes. - Samuel Stanton; Wesley J. Maddox; Ian A. Delbridge; Andrew Gordon Wilson (AISTATS, 2021)

On the model-based stochastic value gradient for continuous reinforcement learning. - Brandon Amos; Samuel Stanton; Denis Yarats; Andrew Gordon Wilson (L4DC, 2021)

Conditioning Sparse Variational Gaussian Processes for Online Decision-making. - Wesley J. Maddox; Samuel Stanton; Andrew Gordon Wilson (NeurIPS, 2021)

Does Knowledge Distillation Really Work? - Samuel Stanton; Pavel Izmailov; Polina Kirichenko; Alexander A. Alemi; Andrew Gordon Wilson (NeurIPS, 2021)

Deconstructing the Inductive Biases of Hamiltonian Neural Networks. - Nate Gruver; Marc Anton Finzi; Samuel Don Stanton; Andrew Gordon Wilson (ICLR, 2022)

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. - Samuel Stanton; Wesley J. Maddox; Nate Gruver; Phillip M. Maffettone; Emily Delaney; Peyton Greenside; Andrew Gordon Wilson (ICML, 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)

Bayesian Optimization with Conformal Coverage Guarantees. - Samuel Stanton; Wesley J. Maddox; Andrew Gordon Wilson (CoRR, 2022)

Bayesian Optimization with Conformal Prediction Sets. - Samuel Stanton; Wesley J. Maddox; Andrew Gordon Wilson (AISTATS, 2023)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Protein Design with Guided Discrete Diffusion. - Nate Gruver; Samuel Stanton; Nathan C. Frey; Tim G. J. Rudner; Isidro Hötzel; Julien Lafrance-Vanasse; Arvind Rajpal; Kyunghyun Cho; Andrew Gordon Wilson (NeurIPS, 2023)

Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them). - Drew Prinster; Samuel Don Stanton; Anqi Liu; Suchi Saria (ICML, 2024)

Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms. - Samuel Stanton; Robert G. Alberstein; Nathan C. Frey; Andrew M. Watkins; Kyunghyun Cho (CoRR, 2024)

LLMs are Highly-Constrained Biophysical Sequence Optimizers. - Angelica Chen; Samuel Don Stanton; Robert G. Alberstein; Andrew M. Watkins; Richard Bonneau; Vladimir Gligorijevic; Kyunghyun Cho; Nathan C. Frey (CoRR, 2024)

Concept Bottleneck Language Models For protein design. - Aya Abdelsalam Ismail; Tuomas P. Oikarinen; Amy Wang; Julius Adebayo; Samuel Stanton; Taylor Joren; Joseph Kleinhenz; Allen Goodman; Héctor Corrada Bravo; Kyunghyun Cho; Nathan C. Frey (CoRR, 2024)

Concept Bottleneck Language Models For Protein Design. - Aya Abdelsalam Ismail; Tuomas P. Oikarinen; Amy Wang; Julius Adebayo; Samuel Don Stanton; Héctor Corrada Bravo; Kyunghyun Cho; Nathan C. Frey (ICLR, 2025)