Samuel Stanton

According to our database, Samuel Stanton authored at least 12 papers between 2020 and 2023.

Collaborative distances:

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

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

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

Bayesian Optimization with Conformal Prediction Sets.

Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Bayesian Optimization with Conformal Coverage Guarantees.

Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson

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

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

Proceedings of the International Conference on Machine Learning, 2022

Deconstructing the Inductive Biases of Hamiltonian Neural Networks.

Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Does Knowledge Distillation Really Work?

Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Conditioning Sparse Variational Gaussian Processes for Online Decision-making.

Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the model-based stochastic value gradient for continuous reinforcement learning.

Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Kernel Interpolation for Scalable Online Gaussian Processes.

Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data.

Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson

Proceedings of the 37th International Conference on Machine Learning, 2020