| 2026 | ACL | PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation. | Junho Park, Dohoon Kim, Taesup Moon |
| 2025 | ACL | DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning. | Dohoon Kim, Donghun Kang, Taesup Moon |
| 2025 | CVPR | Multi-Group Proportional Representations for Text-to-Image Models. | Sangwon Jung, Alex Oesterling, Claudio Mayrink Verdun, Sajani Vithana, Taesup Moon, Flvio P. Calmon |
| 2025 | ICCV | MA-CIR: A Multimodal Arithmetic Benchmark for Composed Image Retrieval. | Jaeseok Byun, Young Kyun Jang, Seokhyeon Jeong, Donghyun Kim, Taesup Moon |
| 2025 | ICCV | An Efficient Post-Hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image Retrieval. | Jaeseok Byun, Seokhyeon Jeong, Wonjae Kim, Sanghyuk Chun, Taesup Moon |
| 2025 | ICLR | Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline. | Hongjoon Ahn, Jinu Hyeon, Youngmin Oh, Bosun Hwang, Taesup Moon |
| 2025 | WACV | TLDR: Text Based Last-Layer Retraining for Debiasing Image Classifiers. | Juhyeon Park, Seokhyeon Jeong, Taesup Moon |
| 2024 | AAAI | Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers. | Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, Moontae Lee |
| 2024 | CVPR | MAFA: Managing False Negatives for Vision-Language Pre-Training. | Jaeseok Byun, Dohoon Kim, Taesup Moon |
| 2024 | ICLR | Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline. | Donggyu Lee, Sangwon Jung, Taesup Moon |
| 2024 | ICML | Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning. | Sungmin Cha, Kyunghyun Cho, Taesup Moon |
| 2024 | ICML | Listwise Reward Estimation for Offline Preference-based Reinforcement Learning. | Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon |
| 2024 | WACV | NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations. | Sungmin Cha, Naeun Ko, Heewoong Choi, Youngjoon Yoo, Taesup Moon |
| 2023 | AAAI | Towards More Robust Interpretation via Local Gradient Alignment. | Sunghwan Joo, Seokhyeon Jeong, Juyeon Heo, Adrian Weller, Taesup Moon |
| 2023 | AAAI | Issues for Continual Learning in the Presence of Dataset Bias. | Donggyu Lee, Sangwon Jung, Taesup Moon |
| 2023 | CVPR | Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning. | Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong, Moontae Lee, Taesup Moon |
| 2023 | ICLR | Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization. | Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon |
| 2022 | CVPR | Learning Fair Classifiers with Partially Annotated Group Labels. | Sangwon Jung, Sanghyuk Chun, Taesup Moon |
| 2022 | ECCV | GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training. | Jaeseok Byun, Taebaek Hwang, Jianlong Fu, Taesup Moon |
| 2021 | CVPR | FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. | Jaeseok Byun, Sungmin Cha, Taesup Moon |
| 2021 | CVPR | Fair Feature Distillation for Visual Recognition. | Sangwon Jung, Donggyu Lee, Taeeon Park, Taesup Moon |
| 2021 | ICCV | SS-IL: Separated Softmax for Incremental Learning. | Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, Taesup Moon |
| 2021 | ICLR | CPR: Classifier-Projection Regularization for Continual Learning. | Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flvio P. Calmon, Taesup Moon |
| 2021 | ICLR | GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images. | Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon |
| 2020 | AISTATS | Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel. | Taeeon Park, Taesup Moon |
| 2020 | ICML | xxAI - Beyond Explainable Artificial Intelligence. | Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Mller, Wojciech Samek |
| 2020 | UAI | Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty. | Hongjoon Ahn, Taesup Moon |
| 2019 | AAAI | DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling. | Sunghwan Joo, Sungmin Cha, Taesup Moon |
| 2019 | AAAI | Subtask Gated Networks for Non-Intrusive Load Monitoring. | Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, Wonjong Rhee |
| 2019 | ICCV | Fully Convolutional Pixel Adaptive Image Denoiser. | Sungmin Cha, Taesup Moon |
| 2018 | ICASSP | Neural Adaptive Image Denoiser. | Sungmin Cha, Taesup Moon |
| 2015 | ASRU | RNNDROP: A novel dropout for RNNS in ASR. | Taesup Moon, Heeyoul Choi, Hoshik Lee, Inchul Song |
| 2011 | ISIT | Discrete denoising of heterogeneous two-dimensional data. | Taesup Moon, Tsachy Weissman, Jae-Young Kim |
| 2011 | WWW | Learning to model relatedness for news recommendation. | Yuanhua Lv, Taesup Moon, Pranam Kolari, Zhaohui Zheng, Xuanhui Wang, Yi Chang |
| 2010 | CIKM | User behavior driven ranking without editorial judgments. | Taesup Moon, Georges Dupret, Shihao Ji, Ciya Liao, Zhaohui Zheng |
| 2010 | CIKM | Online learning for recency search ranking using real-time user feedback. | Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohui Zheng, Yi Chang |
| 2010 | WSDM | IntervalRank: isotonic regression with listwise and pairwise constraints. | Taesup Moon, Alexander J. Smola, Yi Chang, Zhaohui Zheng |
| 2007 | ISIT | Competitive On-line Linear FIR MMSE Filtering. | Taesup Moon, Tsachy Weissman |
| 2006 | ISNN | Soft Sensor Using PNN Model and Rule Base for Wastewater Treatment Plant. | Yejin Kim, Hyeon Bae, Kyungmin Poo, Jongrack Kim, Taesup Moon, Sungshin Kim, Changwon Kim |
| 2005 | ISIT | Discrete universal filtering via hidden Markov modelling. | Taesup Moon, Tsachy Weissman |