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Found 10 papers, 6 papers with code
Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences
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.
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks
Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension.
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
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.
Sparse Bayesian Optimization
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions.
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
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.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
Bayesian Optimization for Policy Search via Online-Offline Experimentation
To alleviate these constraints, we augment online experiments with an offline simulator and apply multi-task Bayesian optimization to tune live machine learning systems.
Practical Transfer Learning for Bayesian Optimization
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.
Constrained Bayesian Optimization with Noisy Experiments
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems.
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists.
