Jungtaek Kim

Orcid: 0000-0002-1905-1399

Affiliations:
  • University of Pittsburgh, PA, USA
  • Pohang University of Science and Technology (POSTECH), Department of Computer Science and Engineering, South Korea (former)


According to our database1, Jungtaek Kim authored at least 25 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
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Links

Online presence:

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Bibliography

2023
BayesO: A Bayesian optimization framework in Python.
J. Open Source Softw., October, 2023

Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions.
CoRR, 2023

Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning.
CoRR, 2023

Generative Neural Fields by Mixtures of Neural Implicit Functions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Sequential Brick Assembly with Efficient Constraint Satisfaction.
CoRR, 2022

Combinatorial Bayesian optimization with random mapping functions to convex polytopes.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Learning to Assemble Geometric Shapes.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

On Evaluation Metrics for Graph Generative Models.
Proceedings of the Tenth International Conference on Learning Representations, 2022

On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Bayesian optimization with approximate set kernels.
Mach. Learn., 2021

Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope.
CoRR, 2020

Combinatorial 3D Shape Generation via Sequential Assembly.
CoRR, 2020

On Local Optimizers of Acquisition Functions in Bayesian Optimization.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Bootstrapping neural processes.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Bayesian Optimization over Sets.
CoRR, 2019

Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization.
CoRR, 2019

MxML: Mixture of Meta-Learners for Few-Shot Classification.
CoRR, 2019

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Set Transformer.
CoRR, 2018

Clustering-Guided Gp-Ucb for Bayesian Optimization.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

On the Optimal Bit Complexity of Circulant Binary Embedding.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Learning to Transfer Initializations for Bayesian Hyperparameter Optimization.
CoRR, 2017


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