Kyungwoo Song

Kyungwoo Song (송경우)

PhD Candidate
Applied Artificial Intelligence Lab
Industrial & Systems Engineering
KAIST

kyungwoo.song@gmail.com / gtshs2@kaist.ac.kr
[CV] [Research Statement] [Google Scholar]
[News Letter] [GitHub] [LinkedIn]



News

  • Dec. 2020: I gave a talk at IBS!
  • Dec. 2020: Our two papers are accepted at AAAI 2021!
  • Nov. 2020: I successfully defended my Ph.D. thesis!
  • Oct. 2020: I gave a talk at Postech!
  • Sep. 2020: Our one paper is accepted at Findings of EMNLP 2020!
  • Aug. 2020: I gave a talk at ANU!
  • Jul. 2020: Our one paper is accepted at CIKM 2020!
  • Apr. 2020: Our one paper is accepted at IJCAI-PRICAI 2020 Doctoral Consortium!
  • Nov. 2019: Our three papers are accepted at AAAI 2020!
  • Nov. 2018: Our two papers are accepted at AAAI 2019!

  • Education

    KAIST (From 2017.03)

  • PhD Candidate in Industrial & Systems Engineering
  • Advisor: Il-Chul Moon
  • Thesis: Context-Aware Model with Generalized Structured Gate and Attention

  • KAIST (2015.03-2017.02)
  • Master Degree in Industrial & Systems Engineering
  • Advisor: Il-Chul Moon
  • Thesis: Deep Ideal Point Estimation with Network

  • KAIST (2010.02 ~ 2015.02)
  • Bachelor of Science in Mathematical Sciences
  • Bachelor of Science in Industrial & Systems Engineering

  • Publications

    Peer-Reviewed Papers

    • Implicit Kernel Attention
      Kyungwoo Song, Yohan Jung, Dongjun Kim, Il-Chul Moon
      AAAI 2021
      [paper]

    • Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
      Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song, Weonyoung Joo, Wanmo Kang, Il-Chul Moon.
      AAAI 2021
      [paper]

    • Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
      Seungjae Shin, Kyungwoo Song, JoonHo Jang, Hyemi Kim, Weonyoung Joo, Il-Chul Moon
      Findings of EMNLP 2020
      [paper]

    • Deep Generative Positive-Unlabeled Learning under Selection Bias
      ByeongHu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoonyeong Kim, Il-Chul Moon
      CIKM 2020
      [paper]

    • Context Aware Sequence Modeling
      Kyungwoo Song
      IJCAI 2020 Doctoral Consortium
      [paper]

    • Bivariate Beta-LSTM
      Kyungwoo Song, JoonHo Jang, Seung jae Shin, Il-Chul Moon
      AAAI 2020
      [paper]

    • Hierarchically Clustered Representation Learning
      Su-Jin Shin, Kyungwoo Song, Il-Chul Moon
      AAAI 2020
      [paper]

    • Sequential Recommendation with Relation-Aware Kernelized Self-Attention
      Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoonyeong Kim, Il-Chul Moon
      AAAI 2020
      [paper]

    • Hierarchical Context enabled Recurrent Neural Network for Recommendation
      Kyungwoo Song*, Mingi Ji*, Sungrae Park, Il-Chul Moon
      AAAI 2019
      [paper] [code]

    • Adversarial Dropout for Recurrent Neural Networks
      Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, Il-Chul Moon
      AAAI 2019
      [paper] [code]

    • Ballistic Coefficient Estimation with Gaussian Process Particle Filter
      Il-Chul Moon, Jinhyung Tak, Sang-Hyeon Kim, Kyungwoo Song
      ICCAS 2018
      [paper]

    • Neural Ideal Point Estimation Network
      Kyungwoo Song, Wonsung Lee, Il-Chul Moon
      AAAI 2018
      [paper] [code]

    • State Prediction of High-speed Ballistic Vehicles with Gaussian Process
      Il-Chul Moon, Kyungwoo Song, Sang-Hyeon Kim, Han-Lim Choi
      IJCAS 2018
      [paper]

    • Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information
      Wonsung Lee, Kyungwoo Song, Il-Chul Moon
      CIKM 2017
      [paper]

    • Data-driven ballistic coefficient learning for future state prediction of high-speed vehicles
      Kyungwoo Song, Sang-Hyeon Kim, Jinhyung Tak, Han-Lim Choi, Il-Chul Moon
      FUSION 2016
      [paper] [slide]

    • Identifying the evolution of disasters and responses with network-text analysis
      Kyungwoo Song, Do-Hyeong Kim, Su-Jin Shin, Il-Chul Moon
      SMC 2014
      [paper] [slide]

    Under Review

    • Sequential Likelihood-Free Inference with Implicit Surrogate Proposal
      Dongjun Kim, Kyungwoo Song, Yoonyeong Kim, Yongjin Shin, Il-Chul Moon
      [paper]

    • Approximate Inference for Spectral Mixture Kernel
      Yohan Jung, Kyungwoo Song, Jinkyoo Park
      [paper]

    • Adversarial Likelihood-Free Inference on Black-Box Generator
      Dongjun Kim, Weonyoung Joo, Seungjae Shin, Kyungwoo Song, Il-Chul Moon
      [paper]

    • Look-Ahead Acquisition with Informative Mixup for Active Learning
      Yooon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon
      [paper]

    * denotes equal contribution.

    Awards Scholarships

  • IJCAI-PRICAI2020 Volunteers and Grants Program
  • KAKAO Research Supporting Program, 2018
  • AAAI Student Scholar, 2018
  • SMC Student Travel Grant, 2014

  • Services

  • Senior Program Committee Member: IJCAI 2021
  • Program Committee Member: IJCAI 2020, ACL 2020, CMOT 2020, EMNLP 2020, Neurips 2020, AAAI 2021, ICLR 2021, AISTATS 2021, ICML 2021