150 citations · 221 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
MxML: Mixture of Meta-Learners for Few-Shot Classification
Minseop Park, Jungtaek Kim, Saehoon Kim +2
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples. Most of…
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…