no code implementations • 9 Jun 2023 • Michael Shvartsman, Benjamin Letham, Stephen Keeley
Models for human choice prediction in preference learning and psychophysics often consider only binary response data, requiring many samples to accurately learn preferences or perceptual detection thresholds.
no code implementations • 2 Feb 2023 • Stephen Keeley, Benjamin Letham, Chase Tymms, Craig Sanders, Michael Shvartsman
Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension.
1 code implementation • 18 Mar 2022 • Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman
We demonstrate a clear benefit to using this new class of acquisition functions on benchmark problems, and on a challenging real-world task of estimating a high-dimensional contrast sensitivity function.
1 code implementation • 3 Mar 2022 • Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions.
1 code implementation • NeurIPS 2020 • Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy
We show empirically that properly addressing these issues significantly improves the efficacy of linear embeddings for BO on a range of problems, including learning a gait policy for robot locomotion.
2 code implementations • NeurIPS 2020 • Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
no code implementations • 1 Apr 2019 • Benjamin Letham, Eytan Bakshy
To alleviate these constraints, we augment online experiments with an offline simulator and apply multi-task Bayesian optimization to tune live machine learning systems.
2 code implementations • 6 Feb 2018 • Matthias Feurer, Benjamin Letham, Frank Hutter, Eytan Bakshy
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset.
no code implementations • 21 Jun 2017 • Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems.
2 code implementations • 5 Nov 2015 • Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists.