Jungtaek Kim's Publications / E-prints / Manuscripts

(* indicates equal contribution.)
  1. Juho Lee*, Yoonho Lee*, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, and Yee Whye Teh (2020),
    "Bootstrapping neural processes" ,
    in Advances in Neural Information Processing Systems 33 (NeurIPS-2020),
    Virtual-only conference, December 6-12, 2020.
    Acceptance rate: 1900/9454 = 20.1%
  2. Jungtaek Kim and Seungjin Choi (2020),
    "On local optimizers of acquisition functions in Bayesian optimization" ,
    in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-2020),
    Virtual-only conference, September 14-18, 2020.
    Acceptance rate: 131/687 = 19.1%
  3. Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi (2019),
    "Bayesian optimization over sets",
    ICML Workshop on Automated Machine Learning (AutoML-2019),
    Long Beach, California, USA, June 14, 2019.
  4. Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, and Yee Whye Teh (2019),
    "Set Transformer: A framework for attention-based permutation-invariant neural networks",
    in Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML-2019),
    Long Beach, California, USA, June 9-15, 2019.
    Acceptance rate: 773/3424 = 22.6%
  5. Jungtaek Kim and Seungjin Choi (2019),
    "Practical Bayesian optimization with threshold-guided marginal likelihood maximization",
    arXiv e-prints, arXiv:1905.07540, May 18, 2019.
  6. Minseop Park, Jungtaek Kim, Saehoon Kim, Yanbin Liu, and Seungjin Choi (2019),
    "MxML: Mixture of meta-learners for few-shot classification",
    arXiv e-prints, arXiv:1904.05658, April 11, 2019.
  7. Minseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, and Seungjin Choi (2018),
    "TAEML: Task-adaptive ensemble of meta-learners",
    NeurIPS Workshop on Meta-Learning (MetaLearn-2018),
    Montreal, Quebec, Canada, December 8, 2018.
  8. Jungtaek Kim and Seungjin Choi (2018),
    "Automated machine learning for soft voting in an ensemble of tree-based classifiers",
    International Workshop on Automatic Machine Learning at ICML/IJCAI-ECAI (AutoML-2018),
    Stockholm, Sweden, July 14, 2018.
  9. Inhyuk Jo, Jungtaek Kim, Hyohyeong Kang, Yong-Deok Kim, and Seungjin Choi (2018),
    "Open set recognition by regularizing classifier with fake data generated by generative adversarial networks",
    in Proceedings of the Forty-Third IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2018),
    Calgary, Alberta, Canada, April 15-20, 2018.
  10. Jungtaek Kim and Seungjin Choi (2018),
    "Clustering-guided GP-UCB for Bayesian optimization",
    in Proceedings of the Forty-Third IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2018),
    Calgary, Alberta, Canada, April 15-20, 2018.
  11. Saehoon Kim, Jungtaek Kim, and Seungjin Choi (2018),
    "On the optimal bit complexity of circulant binary embedding",
    in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018),
    New Orleans, Louisiana, USA, February 2-7, 2018.
    Acceptance rate: 933/3800 = 24.6%
  12. Jungtaek Kim, Saehoon Kim, and Seungjin Choi (2017),
    "Learning to transfer initializations for Bayesian hyperparameter optimization",
    NeurIPS Workshop on Bayesian Optimization (BayesOpt-2017),
    Long Beach, California, USA, December 9, 2017.
  13. Jungtaek Kim, Saehoon Kim, and Seungjin Choi (2017),
    "Learning to warm-start Bayesian hyperparameter optimization",
    arXiv e-prints, arXiv:1710.06219, October 17, 2017.
  14. Jungtaek Kim, Jongheon Jeong, and Seungjin Choi (2016),
    "AutoML Challenge: AutoML framework using random space partitioning optimizer",
    ICML Workshop on Automatic Machine Learning (AutoML-2016),
    New York, New York, USA, June 24, 2016.