Kyungwoo Song

Research output

Research output per year 2014 2018 2020 2021 2022

• 16 Conference contribution
• 1 Article
• 1 Conference article

Research output per year

Research output per year

Search results

• 2022

  Learning Fair Representation via Distributional Contrastive Disentanglement

  Oh, C., Won, H., So, J., Kim, T., Kim, Y., Choi, H. & Song, K., 2022 Aug 14, KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 1295-1305 11 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Representation 100%
  • Generalization 20%
  • Simulation Mode 20%
  • Sensitive Informations 20%
  • Superior Performance 20%
• 2021

  Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder

  Kim, H., Shin, S., Jang, J. H., Song, K., Joo, W., Kang, W. & Moon, I. C., 2021, 35th AAAI Conference on Artificial Intelligence, AAAI 2021. Association for the Advancement of Artificial Intelligence, p. 8128-8136 9 p. (35th AAAI Conference on Artificial Intelligence, AAAI 2021; vol. 9B).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Information 100%
  • Autoencoder 83%
  • Sensitive Informations 50%
  • Classification 33%
  • Causal Inference 33%
  9 Citations (Scopus)

• Implicit Kernel Attention

  Song, K., Jung, Y., Kim, D. & Moon, I. C., 2021, 35th AAAI Conference on Artificial Intelligence, AAAI 2021. Association for the Advancement of Artificial Intelligence, p. 9713-9721 9 p. (35th AAAI Conference on Artificial Intelligence, AAAI 2021; vol. 11A).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Attention 100%
  • Transformer 21%
  • Norm 21%
  • Attention (Machine Learning) 21%
  • Implicit 21%
  3 Citations (Scopus)

• LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning

  Kim, Y. Y., Song, K., Jang, J. & Moon, I. C., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 22919-22930 12 p. (Advances in Neural Information Processing Systems; vol. 27).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Active Learning 100%
  • Data Instance 100%
  • Virtual Instance 100%
  • Functions 66%
  • Experimental Result 33%
• 2020

  Bivariate beta-LSTM

  Song, K., Jang, J. H., Shin, S. J. & Moon, I. C., 2020, AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. AAAI press, p. 5818-5825 8 p. (AAAI 2020 - 34th AAAI Conference on Artificial Intelligence).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Sigmoid Function 100%
  • Long Short-Term Memory Network 100%
  • Modeling 100%
  • Simulation Mode 66%
  • Beta Distribution 66%
  2 Citations (Scopus)

• Context aware sequence model

  Song, K., 2020, Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020. Bessiere, C. (ed.). International Joint Conferences on Artificial Intelligence, p. 5208-5209 2 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2021-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Modeling 100%
  • Contexts 100%
  • Representation 40%
  • Granularity 20%
  • Contextual Information 20%

• Deep Generative Positive-Unlabeled Learning under Selection Bias

  Na, B., Kim, H., Song, K., Joo, W., Kim, Y. Y. & Moon, I. C., 2020 Oct 19, CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, p. 1155-1164 10 p. (International Conference on Information and Knowledge Management, Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Assumptions 100%
  • Biases 66%
  • Selection 66%
  • Real World 33%
  • Simulation Mode 33%
  7 Citations (Scopus)

• Hierarchically clustered representation learning

  Shin, S. J., Song, K. & Moon, I. C., 2020, AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. AAAI press, p. 5776-5783 8 p. (AAAI 2020 - 34th AAAI Conference on Artificial Intelligence).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Representation Learning 100%
  • Embedding 66%
  • Embedding Space 33%
  • Optimization 16%
  • Hierarchical Structures 16%
  3 Citations (Scopus)

• Neutralizing gender bias in word embeddings with latent disentanglement and counterfactual generation

  Shin, S., Song, K., Jang, J. H., Kim, H., Joo, W. & Moon, I. C., 2020, Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020. Association for Computational Linguistics (ACL), p. 3126-3140 15 p. (Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Word Embedding 100%
  • Semantic Information 60%
  • Generation 40%
  • Regularization 20%
  • Information Loss 20%
  8 Citations (Scopus)

• Sequential recommendation with relation-aware kernelized self-attention

  Ji, M., Joo, W., Song, K., Kim, Y. Y. & Moon, I. C., 2020, AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. AAAI press, p. 4304-4311 8 p. (AAAI 2020 - 34th AAAI Conference on Artificial Intelligence).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Attention 100%
  • Simulation Mode 40%
  • Attention (Machine Learning) 20%
  • Covariance Matrix 10%
  • Benchmark 10%
  16 Citations (Scopus)
• 2019

  Adversarial dropout for recurrent neural networks

  Park, S., Song, K., Ji, M., Lee, W. & Moon, I. C., 2019, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI press, p. 4699-4706 8 p. (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Recurrent Neural Network 100%
  • Generalization Performance 14%
  • Classification Task 14%
  • Text Classification 14%
  • Application 14%

• Hierarchical context enabled recurrent neural network for recommendation

  Song, K., Ji, M., Park, S. & Moon, I. C., 2019, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI press, p. 4983-4991 9 p. (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • User 100%
  • Recurrent Neural Network 80%
  • Contexts 80%
  • Transitions 60%
  • Simulation Mode 20%
  21 Citations (Scopus)
• 2018

  Ballistic coefficient estimation with Gaussian process particle filter

  Moon, I. C., Tak, J., Kim, S. H. & Song, K., 2018 Dec 10, International Conference on Control, Automation and Systems. IEEE Computer Society, p. 776-780 5 p. 8571994. (International Conference on Control, Automation and Systems; vol. 2018-October).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Gaussian Process 100%
  • Particle Filter 75%
  • Estimation Accuracy 75%
  • Target State 50%
  • High Speed 50%

• Neural ideal point estimation network

  Song, K., Lee, W. & Moon, I. C., 2018, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, p. 2411-2418 8 p. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Model 100%
  • Simulation Mode 100%
  • Observation 40%
  • Politics 40%
  • Show 20%
  1 Citation (Scopus)
• 2017

  Augmented variational autoencoders for collaborative filtering with auxiliary information

  Lee, W., Song, K. & Moon, I. C., 2017 Nov 6, CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Association for Computing Machinery, p. 1139-1148 10 p. (International Conference on Information and Knowledge Management, Proceedings; vol. Part F131841).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Auxiliary Information 100%
  • Simulation Mode 80%
  • Collaborative Filtering 80%
  • User 60%
  • Autoencoder 60%
  44 Citations (Scopus)
• 2016

  Data-driven ballistic coefficient learning for future state prediction of high-speed vehicles

  Song, K., Kim, S. H., Tak, J., Choi, H. L. & Moon, I. C., 2016 Aug 1, FUSION 2016 - 19th International Conference on Information Fusion, Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 17-24 8 p. 7527864. (FUSION 2016 - 19th International Conference on Information Fusion, Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • State 100%
  • Vehicles 80%
  • High Speed 80%
  • Future State 60%
  • Statistical Inference 40%
  3 Citations (Scopus)