Samuel Stanton

23 papers A* 6A 2Journal 14Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Conformal Policy Control. CoRR Drew Prinster, Clara Fannjiang, Ji Won Park, Kyunghyun Cho, Anqi Liu, Suchi Saria, Samuel Stanton
2024	J	`jnl`	Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms. CoRR Samuel Stanton, Robert G. Alberstein, Nathan C. Frey, Andrew M. Watkins, Kyunghyun Cho
2024	J	`jnl`	Concept Bottleneck Language Models For protein design. CoRR 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
2024	J	`jnl`	Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them). CoRR Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria
2023	A	`conf`	Bayesian Optimization with Conformal Prediction Sets. AISTATS Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson
2023	A*	`conf`	GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS 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
2023	A*	`conf`	Protein Design with Guided Discrete Diffusion. NeurIPS Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
2023	J	`jnl`	Protein Design with Guided Discrete Diffusion. CoRR Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
2022	A*	`conf`	Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
2022	J	`jnl`	Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
2022	J	`jnl`	Bayesian Optimization with Conformal Coverage Guarantees. CoRR Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson
2022	J	`jnl`	Deconstructing the Inductive Biases of Hamiltonian Neural Networks. CoRR Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson
2022	J	`jnl`	PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design. CoRR Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew M. Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho
2021	A*	`conf`	Conditioning Sparse Variational Gaussian Processes for Online Decision-making. NeurIPS Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
2021	J	`jnl`	Conditioning Sparse Variational Gaussian Processes for Online Decision-making. CoRR Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
2021	A*	`conf`	Does Knowledge Distillation Really Work? NeurIPS Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
2021	J	`jnl`	Does Knowledge Distillation Really Work? CoRR Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
2021	A	`conf`	Kernel Interpolation for Scalable Online Gaussian Processes. AISTATS Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson
2021	J	`jnl`	Kernel Interpolation for Scalable Online Gaussian Processes. CoRR Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson
2021	—	`conf`	On the model-based stochastic value gradient for continuous reinforcement learning. L4DC Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson
2020	A*	`conf`	Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. ICML Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
2020	J	`jnl`	Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. CoRR Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
2020	J	`jnl`	On the model-based stochastic value gradient for continuous reinforcement learning. CoRR Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson