5 citations · 9 across the 6 of their papers we have counts for
7 papers · 1 filter
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…
Discrete Infomax Codes for Supervised Representation Learning
Yoonho Lee, Wonjae Kim, Wonpyo Park +1
Learning compact discrete representations of data is a key task on its own or for facilitating subsequent processing of data. In this paper we present a model that produces Discret…
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…
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
Juho Lee, Lancelot F. James, Seungjin Choi +1
We consider a non-projective class of inhomogeneous random graph models with interpretable parameters and a number of interesting asymptotic properties. Using the results of Bollob…