Publications

Conformal Policy Control
Drew Prinster, Clara Fannjiang, Ji Won Park, Kyunghyun Cho, Anqi Liu, Suchi Saria, Samuel Stanton
Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)
https://arxiv.org/abs/2603.02196

Disentangling Multispecific Antibody Function with Graph Neural Networks
Joshua Southern, Changpeng Lu, Santrupti Nerli, Samuel Stanton, Andrew M. Watkins, Franziska Seeger, Frédéric A. Dreyer
arXiv preprint (2026)
https://arxiv.org/abs/2601.23212

CDR Conformation Aware Antibody Sequence Design with ConformAb
Imee Sinha, Samuel Stanton, Stephen Lillington, Sarah Robinson, Santrupti Nerli, Karina Zadorozhny, Joseph Kleinhenz, et al.
bioRxiv preprint (2025)
https://doi.org/10.1101/2025.11.12.688095

Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
Angelica Chen, Samuel Stanton, Frances Ding, Robert G. Alberstein, Andrew M. Watkins, Richard Bonneau, Vladimir Gligorijević, Kyunghyun Cho, Nathan C. Frey
Proceedings of the 42nd International Conference on Machine Learning (ICML 2025)
https://arxiv.org/abs/2410.22296

Concept Bottleneck Language Models for Protein Design
Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang, Julius Adebayo, Samuel Stanton, Taylor Joren, Joseph Kleinhenz, Allen Goodman, Héctor Corrada Bravo, Kyunghyun Cho, Nathan C. Frey
International Conference on Learning Representations 13 (ICLR 2025)
https://arxiv.org/abs/2411.06090

Lab-in-the-Loop Therapeutic Antibody Design with Deep Learning
Nathan C. Frey, Isidro Hötzel, Samuel Stanton, Ryan Kelly, Robert G. Alberstein, et al.
bioRxiv preprint (2025)
https://doi.org/10.1101/2025.02.19.639050

Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
Drew Prinster, Samuel Stanton, Anqi Liu, and Suchi Saria
Proceedings of the 41st International Conference on Machine Learning (ICML 2024)
https://arxiv.org/abs/2405.06627

Protein Design with Guided Discrete Diffusion
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew G. Wilson
Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
https://arxiv.org/abs/2305.20009

Bayesian Optimization with Conformal Prediction Sets
Samuel Stanton, Wesley Maddox, Andrew G. Wilson
International Conference on Artificial Intelligence and Statistics 26 (AISTATS 2023)
https://arxiv.org/abs/2210.12496

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew G. Wilson
International Conference on Machine Learning 39 (ICML 2022)
https://arxiv.org/abs/2203.12742

Deconstructing The Inductive Biases Of Hamiltonian Neural Networks
Nate Gruver, Marc Finzi, Samuel Stanton, Andrew G. Wilson
International Conference on Learning Representations 10 (ICLR 2022)
https://arxiv.org/abs/2202.04836

Robust Reinforcement Learning for Shifting Dynamics During Deployment
Samuel Stanton, Rasool Fakoor, Jonas Mueller, Andrew G. Wilson, Alex Smola
The 2021 NeurIPS Workshop on Safe and Robust Control of Uncertain Systems
[pdf]

Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alex Alemi, Andrew G. Wilson
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
https://arxiv.org/abs/2106.05945

Conditioning Sparse Variational Gaussian Processes for Online Decision-Making
Wesley Maddox, Samuel Stanton, Andrew G. Wilson
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
https://arxiv.org/abs/2110.15172

On the Model-Based Stochastic Value Gradient for Continuous Reinforcement Learning
Brandon Amos, Samuel Stanton, Denis Yarats, Andrew G. Wilson
Learning for Dynamics and Control (L4DC 2021)
https://arxiv.org/abs/2008.12775

Kernel Interpolation for Scalable Online Gaussian Processes
Samuel Stanton, Wesley Maddox, Ian Delbridge, Andrew G. Wilson
International Conference on Artificial Intelligence and Statistics 24 (AISTATS 2021)
https://arxiv.org/abs/2103.01454

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew G. Wilson
International Conference on Machine Learning 37 (ICML 2020)
https://arxiv.org/abs/2002.12880

Probabilistic Machine Learning for Online Decision-Making
Samuel Stanton
NYU Doctoral Dissertation
[pdf]

Beyond the Dublin Regulation: An Algorithm for Redistributing Disproportionate Numbers of Asylum Applications
Samuel Stanton
CU Denver Undergraduate Thesis
[pdf]

Patents

Molecule Design with Multi-Objective Optimization of Partially Ordered, Mixed-Variable Molecular Properties
Ji Won Park, Samuel Stanton, Andrew M. Watkins, Kyunghyun Cho
US Patent 12,580,043 (2026)
https://patents.google.com/patent/US12580043B2