Selected Publications

Lee, W.S., Park, S.R. and Moon, I.-C., 2014. Modeling Multiple Fields of Collective Emotions with Brownian Agent-Based Model. In AAMAS. Paris, France.

2014.01.07 06:56

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Lee, W.S., Park, S.R. and Moon, I.-C., 2014. Modeling Multiple Fields of Collective Emotions with Brownian Agent-Based Model. In AAMAS. Paris, France.

 

Abstract : 

 Understanding the emergence of collective emotions is critical to the analysis of online and offline societies. The agent-based simulation community has developed various social norm models to see the polarization of collective emotions. Yet, a few models have psychological background as fundamentals, as well as statistical validation, and this paper aims at resolving the two challenges. Particularly, this paper models agents as Brownian agents with different parameters of arousal, valence, and preference, which originates from the field of psychology. The Brownian agents enable the agents to become more heterogeneous, while they are simple enough to be understood and expanded. In the simulation, the agents influence and are influenced by multiple fields, or parts of community, of collective emotions. We designed two virtual experiments: one hypothetical setting to generate the emergence, and the other setting to validate the model with a real-world dataset. The first experiment identifies the scenario characteristics of extreme polarizations of collective emotions. The second experiment shows that the simple Brownian agent model is able to generate the real-world case with statistical significance.

 

@inproceedings{Lee:2014:MMF:2615731.2615859,
 author = {Lee, Wonsung and Park, Sungrae and Moon, Il-Chul},
 title = {Modeling Multiple Fields of Collective Emotions with Brownian Agent-based Model},
 booktitle = {Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems},
 series = {AAMAS '14},
 year = {2014},
 isbn = {978-1-4503-2738-1},
 location = {Paris, France},
 pages = {797--804},
 numpages = {8},
 url = {http://dl.acm.org/citation.cfm?id=2615731.2615859},
 acmid = {2615859},
 publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
 address = {Richland, SC},
 keywords = {artificial social systems, social and organizational structure, social simulation},
}

 

Source Website : 

 http://dl.acm.org/citation.cfm?id=2615859

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36 DongHyeok Shin, Seungjae Shin, Il-Chul Moon, Frequency Domain-based Dataset Distillation, Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA, Dec 10-Dec 16, 2023
35 Suhyeon Jo, Donghyeok Shin, Byeonghu Na, JoonHo Jang, and Il-Chul Moon, Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy, ACM International Conference on Information and Knowledge Management (CIKM 2023) file
34 Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, SAAL: Sharpness-Aware Active Learning, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023 file
33 Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon, Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023 file
32 Seungjae Shin, Heesun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon, Loss Curvature Matching for Dataset Selection and Condensation, International Conference on Artificial Intelligence and Statistics (AISTATS 23), Valencia, Spain, Apr 25-27, 2023 file
31 Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon, Maximum Likelihood Training of Implicit Nonlinear Diffusion Model, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022 file
30 JoonHo Jang, Byeonghu Na, Dong Hyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon, Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022 file
29 Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation, International Conference on Machine Learning (ICML 2022) file
28 HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon, From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model, International Conference on Machine Learning (ICML 2022) file
27 Yooon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon, LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning, Neural Information Processing Systems (NeurIPS 2021), Virtual, Dec. 7-10, 2021 file
26 Kim, D., Yun, TS., Moon, IC. et al. Automatic calibration of dynamic and heterogeneous parameters in agent-based models. Auton Agent Multi-Agent Syst 35, 46 (2021). file
25 Mingi Ji, Seungjae Shin, Seunghyun Hwang, Gibeom Park, Il-Chul Moon. Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation. Conference on Computer Vision and Pattern Recognition (CVPR 2021). file
24 Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song, Weonyoung Joo, Wanmo Kang, and Il-Chul Moon, Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder, AAAI Conference on Artificial Intelligence (AAAI 2021) file
23 Kyungwoo Song, Yohan Jung, Dongjun Kim, and Il-Chul Moon, Implicit Kernel Attention, AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, Feb. 2-9 file
22 Byeonghu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoon-Yeong Kim, and Il-Chul Moon, Deep Generative Positive-Unlabeled Learning under Selection Bias, ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, Oct. 19 file
21 Joo, W., Lee, W., Park, S., & Moon, I. C. (2020). Dirichlet variational autoencoder. Pattern Recognition, Vol. 107, 107514. file
20 Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, and Il-Chul Moon, Sequential Recommendation with Relation-Aware Kernelized Self-Attention​. AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
19 Su-Jin Shin, Kyungwoo Song, and Il-Chul Moon, Hierarchically Clustered Representation Learning, AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
18 Kyungwoo Song, JoonHo Jang, Seung jae Shin and Il-Chul Moon, Bivariate Beta-LSTM. AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
17 Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, and Il-Chul Moon, Adversarial Dropout for Recurrent Neural Networks. AAAI Conference on Artificial Intelligence (AAAI 2019). Hawaii. Jan. 27-Feb. 1 file
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