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2020 – today
- 2024
[c3]Dongha Kim, Yongchan Choi, Kunwoong Kim, Ilsang Ohn, Yongdai Kim:
IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples. AAAI 2024: 13104-13112- [c2]Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim:
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models. ICML 2024 - 2023
- [j2]Seonghyeon Kim, Sara Kim, Kunwoong Kim, Yongdai Kim
:
Lq regularization for fair artificial intelligence robust to covariate shift. Stat. Anal. Data Min. 16(3): 237-256 (2023) - [i5]Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim:
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models. CoRR abs/2301.04257 (2023) - [i4]Dongyoon Yang, Kunwoong Kim, Yongdai Kim:
Improving Performance of Semi-Supervised Learning by Adversarial Attacks. CoRR abs/2308.04018 (2023) - 2022
- [j1]Kunwoong Kim, Ilsang Ohn, Sara Kim, Yongdai Kim:
SLIDE: A surrogate fairness constraint to ensure fairness consistency. Neural Networks 154: 441-454 (2022) - [c1]Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. ICML 2022: 11074-11101 - [i3]Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. CoRR abs/2202.02943 (2022) - [i2]Kunwoong Kim, Ilsang Ohn, Sara Kim, Yongdai Kim:
SLIDE: a surrogate fairness constraint to ensure fairness consistency. CoRR abs/2202.03165 (2022) - 2021
- [i1]Dongha Kim, Yongchan Choi, Kunwoong Kim, Yongdai Kim:
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks. CoRR abs/2106.15185 (2021)
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