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Samuel Stanton
Samuel Stanton
Principal Machine Learning Scientist, Genentech
Верификована је имејл адреса на gene.com - Почетна страница
Наслов
Навело
Навело
Година
Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
M Finzi, S Stanton, P Izmailov, AG Wilson
International Conference on Machine Learning, 3165-3176, 2020
3612020
Does knowledge distillation really work?
S Stanton, P Izmailov, P Kirichenko, AA Alemi, AG Wilson
Advances in Neural Information Processing Systems 34, 6906-6919, 2021
2572021
Protein design with guided discrete diffusion
N Gruver, S Stanton, N Frey, TGJ Rudner, I Hotzel, J Lafrance-Vanasse, ...
Advances in neural information processing systems 36, 12489-12517, 2023
1162023
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
S Stanton, W Maddox, N Gruver, P Maffettone, E Delaney, P Greenside, ...
Proceedings of the 39th International Conference on Machine Learning, PMLR …, 2022
1012022
On the model-based stochastic value gradient for continuous reinforcement learning
B Amos, S Stanton, D Yarats, AG Wilson
Learning for Dynamics and Control, 6-20, 2021
732021
Deconstructing The Inductive Biases Of Hamiltonian Neural Networks
N Gruver, M Finzi, S Stanton, AG Wilson
arXiv preprint arXiv:2202.04836, 2022
452022
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
WJ Maddox, S Stanton, AG Wilson
Advances in Neural Information Processing Systems 34, 6365-6379, 2021
412021
Kernel Interpolation for Scalable Online Gaussian Processes
S Stanton, W Maddox, I Delbridge, AG Wilson
International Conference on Artificial Intelligence and Statistics, 3133-3141, 2021
412021
Gauche: A library for gaussian processes in chemistry
RR Griffiths, L Klarner, HB Moss, A Ravuri, S Truong, S Stanton, G Tom, ...
arXiv preprint arXiv:2212.04450, 2022
352022
Bayesian Optimization with Conformal Prediction Sets
S Stanton, W Maddox, AG Wilson
International Conference on Artificial Intelligence and Statistics, 959-986, 2023
27*2023
Effective Surrogate Models for Protein Design with Bayesian Optimization
N Gruver, S Stanton, P Kirichenko, M Finzi, P Maffettone, V Myers, ...
2021 ICML Workshop on Computational Biology, 2021
222021
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
JW Park, S Stanton, S Saremi, A Watkins, H Dwyer, V Gligorijevic, ...
arXiv preprint arXiv:2210.04096, 2022
122022
Robust reinforcement learning for shifting dynamics during deployment
S Stanton, R Fakoor, J Mueller, AG Wilson, A Smola
52021
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
D Prinster, SD Stanton, A Liu, S Saria
Forty-first International Conference on Machine Learning, 0
5*
LLMs are highly-constrained biophysical sequence optimizers
A Chen, SD Stanton, RG Alberstein, AM Watkins, R Bonneau, ...
arXiv preprint arXiv:2410.22296, 2024
22024
Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms
S Stanton, R Alberstein, N Frey, A Watkins, K Cho
arXiv preprint arXiv:2407.00236, 2024
12024
Identifying regularization schemes that make feature attributions faithful
J Adebayo, S Stanton, S Kelow, MMR Bonneau, V Gligorijevic, K Cho, ...
NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development, 2023
12023
Model-based Policy Gradients with Entropy Exploration through Sampling
S Stanton, KA Wang, AG Wilson
International Conference on Machine Learning, Generative Modeling and Model …, 2019
1*2019
Concept Bottleneck Language Models For protein design
AA Ismail, T Oikarinen, A Wang, J Adebayo, S Stanton, T Joren, ...
arXiv preprint arXiv:2411.06090, 2024
2024
Probabilistic Machine Learning for Online Decision-Making
S Stanton
New York University, 2022
2022
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