Discrete universal filtering via hidden Markov modelling. - Taesup Moon; Tsachy Weissman (ISIT, 2005)

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 (ISNN (2), 2006)

Competitive On-line Linear FIR MMSE Filtering. - Taesup Moon; Tsachy Weissman (ISIT, 2007)

Universal Filtering Via Hidden Markov Modeling. - Taesup Moon; Tsachy Weissman (IEEE Trans. Inf. Theory, 2008)

Discrete denoising with shifts. - Taesup Moon; Tsachy Weissman (IEEE Trans. Inf. Theory, 2009)

Universal FIR MMSE Filtering. - Taesup Moon; Tsachy Weissman (IEEE Trans. Signal Process., 2009)

User behavior driven ranking without editorial judgments. - Taesup Moon; Georges Dupret; Shihao Ji 001; Ciya Liao; Zhaohui Zheng 001 (CIKM, 2010)

Online learning for recency search ranking using real-time user feedback. - Taesup Moon; Lihong Li 001; Wei Chu; Ciya Liao; Zhaohui Zheng 001; Yi Chang 001 (CIKM, 2010)

IntervalRank: isotonic regression with listwise and pairwise constraints. - Taesup Moon; Alexander J. Smola; Yi Chang 001; Zhaohui Zheng 001 (WSDM, 2010)

Discrete denoising of heterogenous two-dimensional data - Taesup Moon; Tsachy Weissman; Jae-Young Kim 003 (CoRR, 2010)

Discrete denoising of heterogeneous two-dimensional data. - Taesup Moon; Tsachy Weissman; Jae-Young Kim 003 (ISIT, 2011)

Learning to model relatedness for news recommendation. - Yuanhua Lv; Taesup Moon; Pranam Kolari; Zhaohui Zheng 001; Xuanhui Wang; Yi Chang 001 (WWW, 2011)

Refining Recency Search Results with User Click Feedback - Taesup Moon; Wei Chu; Lihong Li 001; Zhaohui Zheng 001; Yi Chang 001 (CoRR, 2011)

Bandits with Generalized Linear Models. - Lihong Li 001; Wei Chu; John Langford 001; Taesup Moon; Xuanhui Wang (ICML On-line Trading of Exploration and Exploitation, 2012)

An Online Learning Framework for Refining Recency Search Results with User Click Feedback. - Taesup Moon; Wei Chu; Lihong Li 001; Zhaohui Zheng 001; Yi Chang 001 (ACM Trans. Inf. Syst., 2012)

Universal Switching FIR Filtering. - Taesup Moon (IEEE Trans. Signal Process., 2012)

Exploiting User Preference for Online Learning in Web Content Optimization Systems. - Jiang Bian 002; Bo Long; Lihong Li 001; Taesup Moon; Anlei Dong; Yi Chang 001 (ACM Trans. Intell. Syst. Technol., 2014)

RNNDROP: A novel dropout for RNNS in ASR. - Taesup Moon; Heeyoul Choi; Hoshik Lee; Inchul Song (ASRU, 2015)

DUDE-Seq: Fast Universal Denoising of Nucleotide Sequences. - Byunghan Lee; Taesup Moon; Sungroh Yoon; Tsachy Weissman (CoRR, 2015)

Evaluation of a MISR-Based High-Resolution Aerosol Retrieval Method Using AERONET DRAGON Campaign Data. - Taesup Moon; Yueqing Wang; Yang Liu 037; Bin Yu (IEEE Trans. Geosci. Remote. Sens., 2015)

Neural Universal Discrete Denoiser. - Taesup Moon; Seonwoo Min; Byunghan Lee; Sungroh Yoon (NIPS, 2016)

Regularization and Kernelization of the Maximin Correlation Approach. - Taehoon Lee 001; Taesup Moon; Seung Jean Kim; Sungroh Yoon (IEEE Access, 2016)

Neural Universal Discrete Denoiser. - Taesup Moon; Seonwoo Min (CoRR, 2016)

Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks. - Youngwook Kim; Taesup Moon (IEEE Geosci. Remote. Sens. Lett., 2016)

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks. - Jinhee Park; Rios Jesus Javier; Taesup Moon; Youngwook Kim (Sensors, 2016)

3D Densely Convolutional Networks for Volumetric Segmentation. - Toan Duc Bui; Jitae Shin; Taesup Moon (CoRR, 2017)

Uniform Concentration of the Loss Estimator for Neural DUDE. - Taesup Moon (CoRR, 2017)

Neural Affine Grayscale Image Denoising. - Sungmin Cha; Taesup Moon (CoRR, 2017)

Neural Adaptive Image Denoiser. - Sungmin Cha; Taesup Moon (ICASSP, 2018)

UDLR Convolutional Network for Adaptive Image Denoiser. - Sungmin Cha; Taesup Moon (RiTA, 2018)

DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling. - Sunghwan Joo; Sungmin Cha; Taesup Moon (AAAI, 2019)

Subtask Gated Networks for Non-Intrusive Load Monitoring. - Changho Shin; Sunghwan Joo; Jaeryun Yim; Hyoseop Lee; Taesup Moon; Wonjong Rhee (AAAI, 2019)

Fully Convolutional Pixel Adaptive Image Denoiser. - Sungmin Cha; Taesup Moon (ICCV, 2019)

Uncertainty-based Continual Learning with Adaptive Regularization. - Hongjoon Ahn; Sungmin Cha; Donggyu Lee; Taesup Moon (NeurIPS, 2019)

Fooling Neural Network Interpretations via Adversarial Model Manipulation. - Juyeon Heo; Sunghwan Joo; Taesup Moon (NeurIPS, 2019)

Working Vacation Scheduling of MX/M/1/N System using Neural Network. - Yongbee Park; Taesup Moon (RiTA, 2019)

Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation. - Toan Duc Bui; Jitae Shin; Taesup Moon (Biomed. Signal Process. Control., 2019)

GAN2GAN: Generative Noise Learning for Blind Image Denoising with Single Noisy Images. - Sungmin Cha; Taeeon Park; Taesup Moon (CoRR, 2019)

Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel. - Taeeon Park; Taesup Moon (AISTATS, 2020)

xxAI - Beyond Explainable Artificial Intelligence. - Andreas Holzinger; Randy Goebel; Ruth Fong; Taesup Moon; Klaus-Robert Müller; Wojciech Samek (xxAI@ICML, 2020)

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. - Sangwon Jung; Hongjoon Ahn; Sungmin Cha; Taesup Moon (NeurIPS, 2020)

Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty. - Hongjoon Ahn; Taesup Moon (UAI, 2020)

Adaptive Group Sparse Regularization for Continual Learning. - Sangwon Jung; Hongjoon Ahn; Sungmin Cha; Taesup Moon (CoRR, 2020)

A Simple Class Decision Balancing for Incremental Learning. - Hongjoon Ahn; Taesup Moon (CoRR, 2020)

CPR: Classifier-Projection Regularization for Continual Learning. - Sungmin Cha; Hsiang Hsu; Flávio P. Calmon; Taesup Moon (CoRR, 2020)

Learning Blind Pixelwise Affine Image Denoiser With Single Noisy Images. - Jaeseok Byun; Taesup Moon (IEEE Signal Process. Lett., 2020)

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. - Jaeseok Byun; Sungmin Cha; Taesup Moon (CVPR, 2021)

Fair Feature Distillation for Visual Recognition. - Sangwon Jung; Donggyu Lee; Taeeon Park; Taesup Moon (CVPR, 2021)

SS-IL: Separated Softmax for Incremental Learning. - Hongjoon Ahn; Jihwan Kwak; Subin Lim; Hyeonsu Bang; Hyojun Kim; Taesup Moon (ICCV, 2021)

CPR: Classifier-Projection Regularization for Continual Learning. - Sungmin Cha; Hsiang Hsu; Taebaek Hwang; Flávio P. Calmon; Taesup Moon (ICLR, 2021)

GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images. - Sungmin Cha; Taeeon Park; Byeongjoon Kim; Jongduk Baek; Taesup Moon (ICLR, 2021)

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. - Sungmin Cha; Beomyoung Kim; Youngjoon Yoo; Taesup Moon (NeurIPS, 2021)

Deep Learning Aided Blind Synchronization Word Estimation. - Yong Sung Kil; Jun Min Song; Sang-Hyo Kim; Taesup Moon; Seok-Ho Chang (IEEE Access, 2021)

Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense against Gray- and Black-Box Attack. - Sungmin Cha; Naeun Ko; Youngjoon Yoo; Taesup Moon (CoRR, 2021)

Observations on K-image Expansion of Image-Mixing Augmentation for Classification. - Joonhyun Jeong; Sungmin Cha; Youngjoon Yoo; Sangdoo Yun; Taesup Moon; Jongwon Choi (CoRR, 2021)

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising. - Sungmin Cha; Seonwoo Min; Sungroh Yoon; Taesup Moon (CoRR, 2021)

Learning Fair Classifiers with Partially Annotated Group Labels. - Sangwon Jung; Sanghyuk Chun; Taesup Moon (CVPR, 2022)

GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training. - Jaeseok Byun; Taebaek Hwang; Jianlong Fu; Taesup Moon (ECCV (19), 2022)

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. - Hongjoon Ahn; Yongyi Yang; Quan Gan; Taesup Moon; David P. Wipf (NeurIPS, 2022)

GAN-Based Framework for Unified Estimation of Process-Induced Random Variation in FinFET. - Taeeon Park; Jihwan Kwak; Hongjoon Ahn; Jinwoong Lee; Jaehyuk Lim; Sangho Yu; Changhwan Shin; Taesup Moon (IEEE Access, 2022)

Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning. - Sungmin Cha; Soonwon Hong; Moontae Lee; Taesup Moon (CoRR, 2022)

Is Continual Learning Truly Learning Representations Continually? - Sungmin Cha; Dongsub Shim; Hyunwoo Kim; Moontae Lee; Honglak Lee; Taesup Moon (CoRR, 2022)

Continual Learning of Micro-Doppler Signature-Based Human Activity Classification. - Donggyu Lee; Hyeongmin Park; Taesup Moon; Youngwook Kim (IEEE Geosci. Remote. Sens. Lett., 2022)

Towards More Robust Interpretation via Local Gradient Alignment. - Sunghwan Joo; Seokhyeon Jeong; Juyeon Heo; Adrian Weller; Taesup Moon (AAAI, 2023)

Issues for Continual Learning in the Presence of Dataset Bias. - Donggyu Lee; Sangwon Jung; Taesup Moon (AAAI Bridge Program, 2023)

Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning. - Sungmin Cha; Sungjun Cho; Dasol Hwang; Sunwon Hong; Moontae Lee; Taesup Moon (CVPR, 2023)

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization. - Sangwon Jung; Taeeon Park; Sanghyuk Chun; Taesup Moon (ICLR, 2023)

SwiFT: Swin 4D fMRI Transformer. - Peter Yongho Kim; Junbeom Kwon 002; Sunghwan Joo; Sangyoon Bae; Donggyu Lee; Yoonho Jung; Shinjae Yoo; Jiook Cha; Taesup Moon (NeurIPS, 2023)

Observations on K-Image Expansion of Image-Mixing Augmentation. - Joonhyun Jeong; Sungmin Cha; Jongwon Choi; Sangdoo Yun; Taesup Moon; Youngjoon Yoo (IEEE Access, 2023)

Continual Learning in the Presence of Spurious Correlation. - Donggyu Lee; Sangwon Jung; Taesup Moon (CoRR, 2023)

Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised Representation Learning. - Sungmin Cha; Taesup Moon (CoRR, 2023)

Converting and Smoothing False Negatives for Vision-Language Pre-training. - Jaeseok Byun; Dohoon Kim 002; Taesup Moon (CoRR, 2023)

Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers. - Sungmin Cha; Sungjun Cho; Dasol Hwang; Honglak Lee; Taesup Moon; Moontae Lee (AAAI, 2024)

Towards More Diverse Evaluation of Class Incremental Learning: Representation Learning Perspective. - Sungmin Cha; Jihwan Kwak; Dongsub Shim; Hyunwoo Kim; Moontae Lee; Honglak Lee; Taesup Moon (CoLLAs, 2024)

Towards realistic incremental scenario in class incremental semantic segmentation. - Jihwan Kwak; Sungmin Cha; Taesup Moon (CoLLAs, 2024)

MAFA: Managing False Negatives for Vision-Language Pre-Training. - Jaeseok Byun; Dohoon Kim 002; Taesup Moon (CVPR, 2024)

Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline. - Donggyu Lee; Sangwon Jung; Taesup Moon (ICLR, 2024)

Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning. - Sungmin Cha; Kyunghyun Cho; Taesup Moon (ICML, 2024)

Listwise Reward Estimation for Offline Preference-based Reinforcement Learning. - Heewoong Choi; Sangwon Jung; Hongjoon Ahn; Taesup Moon (ICML, 2024)

Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? - A Theoretical and Empirical Study. - Sangwon Jung; Sumin Yu; Sanghyuk Chun; Taesup Moon (NeurIPS, 2024)

NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations. - Sungmin Cha; Naeun Ko; Heewoong Choi; Youngjoon Yoo; Taesup Moon (WACV, 2024)

Debiased Learning via Composed Conceptual Sensitivity Regularization. - Sunghwan Joo; Taesup Moon (IEEE Access, 2024)

Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning. - Hongjoon Ahn; Jinu Hyeon; Youngmin Oh; Bosun Hwang; Taesup Moon (CoRR, 2024)

Reducing Task Discrepancy of Text Encoders for Zero-Shot Composed Image Retrieval. - Jaeseok Byun; Seokhyeon Jeong; Wonjae Kim; Sanghyuk Chun; Taesup Moon (CoRR, 2024)

DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning. - Dohoon Kim 002; Donghun Kang; Taesup Moon (ACL (1), 2025)

Multi-Group Proportional Representations for Text-to-Image Models. - Sangwon Jung; Alex Oesterling; Claudio Mayrink Verdun; Sajani Vithana; Taesup Moon; Flávio P. Calmon (CVPR, 2025)

Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline. - Hongjoon Ahn; Jinu Hyeon; Youngmin Oh; Bosun Hwang; Taesup Moon (ICLR, 2025)

TLDR: Text Based Last-Layer Retraining for Debiasing Image Classifiers. - Juhyeon Park; Seokhyeon Jeong; Taesup Moon (WACV, 2025)

FairDRO: Group fairness regularization via classwise robust optimization. - Taeeon Park; Sangwon Jung; Sanghyuk Chun; Taesup Moon (Neural Networks, 2025)

Cost-Efficient Continual Learning with Sufficient Exemplar Memory. - Dongkyu Cho; Taesup Moon; Rumi Chunara; Kyunghyun Cho; Sungmin Cha (CoRR, 2025)

SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding. - Juhyeon Park; Peter Yongho Kim; Jiook Cha; Shinjae Yoo; Taesup Moon (CoRR, 2025)

Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning. - Hongjoon Ahn; Heewoong Choi; Jisu Han; Taesup Moon (CoRR, 2025)

Multi-Group Proportional Representation for Text-to-Image Models. - Sangwon Jung; Alex Oesterling; Claudio Mayrink Verdun; Sajani Vithana; Taesup Moon; Flávio P. Calmon (CoRR, 2025)