10 citations · 18 across the 14 of their papers we have counts for
5 papers · 1 filter
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
Kwang-Sung Jun, Jungtaek Kim
Adapting to a priori unknown noise level is a very important but challenging problem in sequential decision-making as efficient exploration typically requires knowledge of the nois…
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Jungtaek Kim, Seungjin Choi
Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization prob…
Bayesian Optimization with Approximate Set Kernels
Jungtaek Kim, Michael McCourt, Tackgeun You +2
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, tr…
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization
Jungtaek Kim, Seungjin Choi
We propose a practical Bayesian optimization method using Gaussian process regression, of which the marginal likelihood is maximized where the number of model selection steps is gu…
On Local Optimizers of Acquisition Functions in Bayesian Optimization
Jungtaek Kim, Seungjin Choi
Bayesian optimization is a sample-efficient method for finding a global optimum of an expensive-to-evaluate black-box function. A global solution is found by accumulating a pair of…