5 citations · 9 across the 6 of their papers we have counts for
14 papers
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…
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Beomjo Shin, Junsu Cho, Hwanjo Yu +1
Multiple Instance Learning (MIL) involves predicting a single label for a bag of instances, given positive or negative labels at bag-level, without accessing to label for each inst…
Neural Complexity Measures
Yoonho Lee, Juho Lee, Sung Ju Hwang +2
While various complexity measures for deep neural networks exist, specifying an appropriate measure capable of predicting and explaining generalization in deep networks has proven…
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…