Publications

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

Conformal Validity Guarantees Exist for Any Data Distribution
Drew Prinster, Samuel Stanton, Angie Liu, and Suchi Saria
Proceedings of the 41st International Conference on Machine Learning
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 37 (NeurIPS 2023)
https://arxiv.org/abs/2305.20009

Bayesian Optimization with Conformal Coverage Guarantees
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 Vision and 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 35 (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 35 (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 Vision and 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]