| 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 | 363 | 2020 |
| 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 | 261 | 2021 |
| 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 | 124 | 2023 |
| 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 | 104 | 2022 |
| 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 | 76 | 2021 |
| Deconstructing The Inductive Biases Of Hamiltonian Neural Networks N Gruver, M Finzi, S Stanton, AG Wilson arXiv preprint arXiv:2202.04836, 2022 | 45 | 2022 |
| 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 | 42 | 2021 |
| 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 | 41 | 2021 |
| 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 | 35 | 2022 |
| Bayesian Optimization with Conformal Prediction Sets S Stanton, W Maddox, AG Wilson International Conference on Artificial Intelligence and Statistics, 959-986, 2023 | 29* | 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 | 22 | 2021 |
| 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 | 12 | 2022 |
| Robust reinforcement learning for shifting dynamics during deployment S Stanton, R Fakoor, J Mueller, AG Wilson, A Smola | 5 | 2021 |
| 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 | 2 | 2024 |
| 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 | 1 | 2024 |
| 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 | 1 | 2023 |
| 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 |
| Lab-in-the-loop therapeutic antibody design with deep learning NC Frey, I Hotzel, SD Stanton, RL Kelly, RG Alberstein, EK Makowski, ... bioRxiv, 2025.02. 19.639050, 2025 | | 2025 |
| 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 |
Andrew Gordon WilsonNew York UniversityE-mailová adresa ověřena na: cims.nyu.edu
