128 citations · 316 across the 6 of their papers we have counts for
6 papers
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier +1
Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate t…
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
Jian Wu, Peter I. Frazier
This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods […
Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson +1
Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimi…
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Jian Wu, Peter I. Frazier
In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architect…
A multi-dimensional SRBM: Geometric views of its product form stationary distribution
J. G. Dai, Masakiyo Miyazawa, Jian Wu
We present a geometric interpretation of a product form stationary distribution for a -dimensional semimartingale reflecting Brownian motion (SRBM) that lives in the nonnegative…
Decomposable stationary distribution of a multidimensional SRBM
J. G. Dai, Masakiyo Miyazawa, Jian Wu
We call a multidimensional distribution to be decomposable with respect to a partition of two sets of coordinates if the original distribution is the product of the marginal distri…