1 code implementation • 22 Oct 2022 • Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson
Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.
no code implementations • 8 Oct 2022 • Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences.
1 code implementation • 23 Mar 2022 • Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.
1 code implementation • ICLR 2022 • Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases.
1 code implementation • NeurIPS 2021 • Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making.
1 code implementation • NeurIPS 2021 • Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks.
2 code implementations • 2 Mar 2021 • Samuel Stanton, Wesley J. Maddox, Ian Delbridge, Andrew Gordon Wilson
Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion.
1 code implementation • 28 Aug 2020 • Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson
For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.
2 code implementations • ICML 2020 • Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
The translation equivariance of convolutional layers enables convolutional neural networks to generalize well on image problems.