Publications
We publish our research in high-impact conferences and journals within the field of Computer Science. We have collaborated top IT tech companies such as AWS AI Labs, Google Research, and NAVER AI Lab.
As of now, we published more than 40 CS top confernces in various domains, including NLP (EMNLP), CV (ICCV, CVPR), ML (NeurIPS, ICLR, ICML, AAAI), DM (KDD, CIKM, ICDM, WWW, SIGMOD).
Asterisk (*) denotes corresponding authors.
International Conferences and Journals
2024
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Learning to Summarize from LLM-generated FeedbackIn arXiv:2410.13116, 2024
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EMNLPWLearning to Verify Summary Facts with Fine-Grained LLM FeedbackIn FEVER Workshop at International Conference on Empirical Methods in Natural Language Processing (Non-archival), 2024
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UniSumEval: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation for LLMsIn International Conference on Empirical Methods in Natural Language Processing (Top Conference, To Appear), 2024
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Exploiting Representation Curvature for Boundary Detection in Time SeriesIn Advances in Neural Information Processing Systems (Top Conference, To Appear), 2024
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Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined HighlightsIn International Conference on European Conference on Computer Vision (Top Conference), 2024
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Prompt-Guided DETR with RoI-Pruned Masked Attention for Open-Vocabulary Object DetectionPattern Recognition (SCIE, IF=8.0, Q1), 2024
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IoTQ-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT SystemsIEEE Internet of Things Journal (SCIE, IF=10.6, Q1), 2024
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FineSurE: Fine-grained Summarization Evaluation using LLMsIn Annual Meeting of the Association for Computational Linguistics (Top Conference), 2024
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Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent EncodersIn Annual Meeting of the Association for Computational Linguistics (Top Conferencer), 2024
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One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual LearningIn International Conference on Machine Learning (Top Conference), 2024
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MAGID: An Automated Pipeline for Generating Synthetic Multi-modal DatasetsIn Annual Conference of the North American Chapter of the Association for Computational Linguistics (Top Conference, Oral), 2024
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TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue SummarizationIn Annual Conference of the North American Chapter of the Association for Computational Linguistics (Top Conference, Poster), 2024
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Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel SelectionIn Annual Conference of the North American Chapter of the Association for Computational Linguistics (Top Conference, Oral), 2024
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Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly DetectionIn Proceedings of The Web Conference (Top Conference), 2024
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Adaptive Shortcut Debiasing for Online Continual LearningIn AAAI Conference on Artificial Intelligence (Top Conference), 2024
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Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and NoiseIn AAAI Conference on Artificial Intelligence (Top Conference), 2024
2023
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Enhancing Abstractiveness of Summarization Models through Calibrated DistillationIn International Conference on Empirical Methods in Natural Language Processing (Top Conference), 2023
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Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel DecodingIn International Conference on Empirical Methods in Natural Language Processing (Top Conference), 2023
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Robust data pruning under label noise via maximizing re-labeling accuracyIn Advances in Neural Information Processing Systems (Top Conference), 2023
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Prompt-Guided Detr with Roi-Pruned Masked Attention for Open-Vocabulary Object DetectionSSRN 4623541, 2023
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ICCV OralGenerating instance-level prompts for rehearsal-free continual learningIn International Conference on Computer Vision (Top Conference), 2023
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Context Consistency Regularization for Label Sparsity in Time SeriesIn International Conference on Machine Learning (Top Conference), 2023
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Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block ReconstructionarXiv preprint arXiv:2303.12557, 2023
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Re-thinking Federated Active Learning based on Inter-class DiversityIn International Conference on Computer Vision and Pattern Recognition (Top Conference), 2023
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Online Boundary-Free Continual Learning by Scheduled Data PriorIn International Conference on Learning Representations (Top Conference), 2023
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Data collection and quality challenges in deep learning: A data-centric ai perspectiveThe VLDB Journal (SCIE, IF=4.2, Q2), 2023
2022
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Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active LearningIn Advances in Neural Information Processing Systems (Top Conference), 2022
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Understanding cross-domain few-shot learning: An experimental studyIn Advances in Neural Information Processing Systems (Top Conference), 2022
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Multi-view POI-level Cellular Trajectory Reconstruction for Digital Contact Tracing of Infectious DiseasesIn International Conference on Data Mining (Top Conference), 2022
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E-CLIP: Large-scale vision-language representation learning in e-commerceIn International Conference on Information and Knowledge Management (Top Conference), 2022
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ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot LearningIn International Conference on Information and Knowledge Management (Top Conference), 2022
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FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated LearningIn International Conference on Information and Knowledge Management (Top Conference), 2022
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Learning from noisy labels with deep neural networks: A surveyIEEE Transactions on Neural Networks and Learning Systems (SCIE, IF=14.255, Q1), 2022
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Time Is MattEr: Temporal Self-supervision for Video TransformersIn International Conference on Machine Learning (Top Conference), 2022
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Dataset condensation via efficient synthetic-data parameterizationIn International Conference on Machine Learning (Top Conference), 2022
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Coherence-based label propagation over time series for accelerated active learningIn International Conference on Learning Representations (Top Conference), 2022
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VIDT: An efficient and effective fully transformer-based object detectorIn International Conference on Learning Representations (Top Conference), 2022
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Meta-learning for online update of recommender systemsIn AAAI Conference on Artificial Intelligence (Top Conference), 2022
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AAAI OralCovid-EENet: Predicting fine-grained impact of COVID-19 on local economiesIn AAAI Conference on Artificial Intelligence (Top Conference), 2022
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Online continual learning on a contaminated data stream with blurry task boundariesIn International Conference on Computer Vision and Pattern Recognition (Top Conference), 2022
2021
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Task-agnostic undesirable feature deactivation using out-of-distribution dataIn Advances in Neural Information Processing Systems (Top Conference), 2021
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Exploiting scene depth for object detection with multimodal transformersIn British Machine Vision Conference, 2021
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KDD OralRobust learning by self-transition for handling noisy labelsIn International Conference on Knowledge Discovery and Data Mining (Top Conference), 2021
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Machine learning robustness, fairness, and their convergenceIn International Conference on Knowledge Discovery and Data Mining (Top Conference), 2021
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Premere: Meta-reweighting via self-ensembling for point-of-interest recommendationIn AAAI Conference on Artificial Intelligence (Top Conference), 2021
2020
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CIKM OralCarpe Diem, Seize the Samples Uncertain" at the Moment" for Adaptive Batch SelectionIn International Conference on Information and Knowledge Management (Top Conference), 2020
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Ada-boundary: accelerating DNN training via adaptive boundary batch selectionMachine Learning (SCIE, IF=5.414, Q2), 2020
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KDD OralHi-COVIDNet: Deep learning approach to predict inbound COVID-19 patients and case study in South KoreaIn International Conference on Knowledge Discovery and Data Mining (Top Conference), 2020
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Revisit Prediction by Deep Survival AnalysisIn Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2020
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WWW OralTRAP: Two-level regularized autoencoder-based embedding for power-law distributed dataIn Proceedings of The Web Conference (Top Conference), 2020
2019
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ICML SpotlightSELFIE: Refurbishing unclean samples for robust deep learningIn International Conference on Machine Learning (Top Conference), 2019
2018
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SIGMOD OralRP-DBSCAN: A superfast parallel DBSCAN algorithm based on random partitioningIn International Conference on Management of Data (Top Conference), 2018
2017
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PAMAE: parallel k-medoids clustering with high accuracy and efficiencyIn International Conference on Knowledge Discovery and Data Mining (Top Conference), 2017