A Note on the Decomposition Methods for Support Vector Regression. - Shuo-Peng Liao; Hsuan-Tien Lin; Chih-Jen Lin (Neural Comput., 2002)

Infinite Ensemble Learning with Support Vector Machines. - Hsuan-Tien Lin; Ling Li (ECML, 2005)

Improving Generalization by Data Categorization. - Ling Li; Amrit Pratap; Hsuan-Tien Lin; Yaser S. Abu-Mostafa (PKDD, 2005)

Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice. - Hsuan-Tien Lin; Ling Li (ALT, 2006)

Ordinal Regression by Extended Binary Classification. - Ling Li; Hsuan-Tien Lin (NIPS, 2006)

Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent. - Ling Li; Hsuan-Tien Lin (IJCNN, 2007)

A note on Platt's probabilistic outputs for support vector machines. - Hsuan-Tien Lin; Chih-Jen Lin; Ruby C. Weng (Mach. Learn., 2007)

Support Vector Machinery for Infinite Ensemble Learning. - Hsuan-Tien Lin; Ling Li (J. Mach. Learn. Res., 2008)

From Ordinal Ranking to Binary Classification. - Hsuan-Tien Lin (2008)

An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes. - Hung-Yi Lo; Kai-Wei Chang; Shang-Tse Chen; Tsung-Hsien Chiang; Chun-Sung Ferng; Cho-Jui Hsieh; Yi-Kuang Ko; Tsung-Ting Kuo; Hung-Che Lai; Ken-Yi Lin; Chia-Hsuan Wang; Hsiang-Fu Yu; et al. (KDD Cup, 2009)

One-sided Support Vector Regression for Multiclass Cost-sensitive Classification. - Han-Hsing Tu; Hsuan-Tien Lin (ICML, 2010)

Cost-Sensitive Classification on Pathogen Species of Bacterial Meningitis by Surface Enhanced Raman Scattering. - Te-Kang Jan; Hsuan-Tien Lin; Hsin-Pai Chen; Tsung-Chen Chern; Chung-Yueh Huang; Bing-Cheng Wen; Chia-Wen Chung; Yung-Jui Li; Ya-Ching Chuang; Li-Li Li; Yu-Jiun Chan; Juen-Kai Wang; et al. (BIBM, 2011)

Unsupervised auxiliary visual words discovery for large-scale image object retrieval. - Yin-Hsi Kuo; Hsuan-Tien Lin; Wen-Huang Cheng; Yi-Hsuan Yang; Winston H. Hsu (CVPR, 2011)

Multi-label Classification with Error-correcting Codes. - Chun-Sung Ferng; Hsuan-Tien Lin (ACML, 2011)

Multi-label Active Learning with Auxiliary Learner. - Chen-Wei Hung; Hsuan-Tien Lin (ACML, 2011)

An Online Boosting Algorithm with Theoretical Justifications. - Shang-Tse Chen; Hsuan-Tien Lin; Chi-Jen Lu (ICML, 2012)

A simple methodology for soft cost-sensitive classification. - Te-Kang Jan; Da-Wei Wang 004; Chi-Hung Lin; Hsuan-Tien Lin (KDD, 2012)

Feature-aware Label Space Dimension Reduction for Multi-label Classification. - Yao-Nan Chen; Hsuan-Tien Lin (NIPS, 2012)

A Linear Ensemble of Individual and Blended Models for Music Rating Prediction. - Po-Lung Chen; Chen-Tse Tsai; Yao-Nan Chen; Ku-Chun Chou; Chun-Liang Li; Cheng-Hao Tsai; Kuan-Wei Wu; Yu-Cheng Chou; Chung-Yi Li; Wei-Shih Lin; Shu-Hao Yu; Rong-Bing Chiu; et al. (KDD Cup, 2012)

Active Learning with Hinted Support Vector Machine. - Chun-Liang Li; Chun-Sung Ferng; Hsuan-Tien Lin (ACML, 2012)

Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation. - Todd G. McKenzie; Chun-Sung Ferng; Yao-Nan Chen; Chun-Liang Li; Cheng-Hao Tsai; Kuan-Wei Wu; Ya-Hsuan Chang; Chung-Yi Li; Wei-Shih Lin; Shu-Hao Yu; Chieh-Yen Lin; Po-Wei Wang; et al. (KDD Cup, 2012)

Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification. - Hsuan-Tien Lin; Ling Li (Neural Comput., 2012)

Multilabel Classification with Principal Label Space Transformation. - Farbound Tai; Hsuan-Tien Lin (Neural Comput., 2012)

Unsupervised Semantic Feature Discovery for Image Object Retrieval and Tag Refinement. - Yin-Hsi Kuo; Wen-Huang Cheng; Hsuan-Tien Lin; Winston H. Hsu (IEEE Trans. Multim., 2012)

Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking. - Wei-Yuan Shen; Hsuan-Tien Lin (ACML, 2013)

Pairwise Regression with Upper Confidence Bound for Contextual Bandit with Multiple Actions. - Ya-Hsuan Chang; Hsuan-Tien Lin (TAAI, 2013)

Active Learning for Multiclass Cost-Sensitive Classification Using Probabilistic Models. - Po-Lung Chen; Hsuan-Tien Lin (TAAI, 2013)

Data Selection Techniques for Large-Scale Rank SVM. - Ken-Yi Lin; Te-Kang Jan; Hsuan-Tien Lin (TAAI, 2013)

Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits. - Chun-Sung Ferng; Hsuan-Tien Lin (IEEE Trans. Neural Networks Learn. Syst., 2013)

Pseudo-reward Algorithms for Contextual Bandits with Linear Payoff Functions. - Ku-Chun Chou; Hsuan-Tien Lin; Chao-Kai Chiang; Chi-Jen Lu (ACML, 2014)

Reduction from Cost-Sensitive Multiclass Classification to One-versus-One Binary Classification. - Hsuan-Tien Lin (ACML, 2014)

Boosting with Online Binary Learners for the Multiclass Bandit Problem. - Shang-Tse Chen; Hsuan-Tien Lin; Chi-Jen Lu (ICML, 2014)

Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. - Chun-Liang Li; Hsuan-Tien Lin (ICML, 2014)

Machine Learning Approaches for Interactive Verification. - Yu-Cheng Chou; Hsuan-Tien Lin (PAKDD (2), 2014)

Improving ranking performance with cost-sensitive ordinal classification via regression. - Yu-Xun Ruan; Hsuan-Tien Lin; Ming-Feng Tsai (Inf. Retr., 2014)

Effective string processing and matching for author disambiguation. - Wei-Sheng Chin; Yong Zhuang; Yu-Chin Juan; Felix Wu; Hsiao-Yu Tung; Tong Yu 001; Jui-Pin Wang; Cheng-Xia Chang; Chun-Pai Yang; Wei-Cheng Chang; Kuan-Hao Huang; Tzu-Ming Kuo; et al. (J. Mach. Learn. Res., 2014)

Contract Bridge Bidding by Learning. - Chun-Yen Ho; Hsuan-Tien Lin (AAAI Workshop: Computer Poker and Imperfect Information, 2015)

Active Learning by Learning. - Wei-Ning Hsu; Hsuan-Tien Lin (AAAI, 2015)

A practical divide-and-conquer approach for preference-based learning to rank. - Han-Jay Yang; Hsuan-Tien Lin (TAAI, 2015)

Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013. - Chun-Liang Li; Yu-Chuan Su; Ting-Wei Lin; Cheng-Hao Tsai; Wei-Cheng Chang; Kuan-Hao Huang; Tzu-Ming Kuo; Shan-Wei Lin; Young-San Lin; Yu-Chen Lu; Chun-Pai Yang; Cheng-Xia Chang; et al. (J. Mach. Learn. Res., 2015)

Active Learning Using Hint Information. - Chun-Liang Li; Chun-Sung Ferng; Hsuan-Tien Lin (Neural Comput., 2015)

Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. - Chun-Liang Li; Hsuan-Tien Lin; Chi-Jen Lu (AISTATS, 2016)

Automatic Bridge Bidding Using Deep Reinforcement Learning. - Chih-Kuan Yeh; Hsuan-Tien Lin (ECAI, 2016)

Can Active Learning Experience Be Transferred? - Hong-Min Chu; Hsuan-Tien Lin (ICDM, 2016)

A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning. - Kuan-Hao Huang; Hsuan-Tien Lin (ICDM, 2016)

Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning. - Yu-An Chung; Hsuan-Tien Lin; Shao-Wen Yang (IJCAI, 2016)

Linear Upper Confidence Bound Algorithm for Contextual Bandit Problem with Piled Rewards. - Kuan-Hao Huang; Hsuan-Tien Lin (PAKDD (2), 2016)

A Simple Unlearning Framework for Online Learning Under Concept Drifts. - Sheng-Chi You; Hsuan-Tien Lin (PAKDD (1), 2016)

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation. - Yu-An Chung; Hsuan-Tien Lin (CoRR, 2016)

Cost-Sensitive Random Pair Encoding for Multi-Label Classification. - Yao-Yuan Yang; Chih-Wei Chang; Hsuan-Tien Lin (CoRR, 2016)

Cyclic Classifier Chain for Cost-Sensitive Multilabel Classification. - Yi-An Lin; Hsuan-Tien Lin (DSAA, 2017)

Cost-Sensitive Encoding for Label Space Dimension Reduction Algorithms on Multi-label Classification. - Kuo-Hsuan Lo; Hsuan-Tien Lin (TAAI, 2017)

libact: Pool-based Active Learning in Python. - Yao-Yuan Yang; Shao-Chuan Lee; Yu-An Chung; Tung-En Wu; Si-An Chen; Hsuan-Tien Lin (CoRR, 2017)

Soft Methodology for Cost-and-error Sensitive Classification. - Te-Kang Jan; Da-Wei Wang 004; Chi-Hung Lin; Hsuan-Tien Lin (CoRR, 2017)

Cost-sensitive label embedding for multi-label classification. - Kuan-Hao Huang; Hsuan-Tien Lin (Mach. Learn., 2017)

Progressive random k-labelsets for cost-sensitive multi-label classification. - Yuping Wu; Hsuan-Tien Lin (Mach. Learn., 2017)

A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning. - Cheng-Yu Hsieh; Yi-An Lin; Hsuan-Tien Lin (AAAI, 2018)

Compatibility Family Learning for Item Recommendation and Generation. - Yong-Siang Shih; Kai-Yueh Chang; Hsuan-Tien Lin; Min Sun (AAAI, 2018)

Rotation-blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-intensity Regression. - Boyo Chen; Buo-Fu Chen; Hsuan-Tien Lin (KDD, 2018)

REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis. - Yu-Shao Peng; Kai-Fu Tang; Hsuan-Tien Lin; Edward Y. Chang (NeurIPS, 2018)

Cost-Sensitive Reference Pair Encoding for Multi-Label Learning. - Yao-Yuan Yang; Kuan-Hao Huang; Chih-Wei Chang; Hsuan-Tien Lin (PAKDD (1), 2018)

Multi-Label Classification with Feature-Aware Cost-Sensitive Label Embedding. - Hsien-Chun Chiu; Hsuan-Tien Lin (TAAI, 2018)

Automatic Bridge Bidding Using Deep Reinforcement Learning. - Chih-Kuan Yeh; Cheng-Yu Hsieh; Hsuan-Tien Lin (IEEE Trans. Games, 2018)

Deep Learning with a Rethinking Structure for Multi-label Classification. - Yao-Yuan Yang; Yi-An Lin; Hong-Min Chu; Hsuan-Tien Lin (ACML, 2019)

Advances in Cost-sensitive Multiclass and Multilabel Classification. - Hsuan-Tien Lin (KDD, 2019)

Attention-based Deep Tropical Cyclone Rapid Intensification Prediction. - Ching-Yuan Bai; Buo-Fu Chen; Hsuan-Tien Lin (MACLEAN@PKDD/ECML, 2019)

Dynamic principal projection for cost-sensitive online multi-label classification. - Hong-Min Chu; Kuan-Hao Huang; Hsuan-Tien Lin (Mach. Learn., 2019)

Annotation cost-sensitive active learning by tree sampling. - Yu-Lin Tsou; Hsuan-Tien Lin (Mach. Learn., 2019)

Learning from Label Proportions with Consistency Regularization. - Kuen-Han Tsai; Hsuan-Tien Lin (ACML, 2020)

Cold-start Active Learning through Self-supervised Language Modeling. - Michelle Yuan; Hsuan-Tien Lin; Jordan L. Boyd-Graber (EMNLP (1), 2020)

Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. - Yu-Ting Chou; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (ICML, 2020)

SERIL: Noise Adaptive Speech Enhancement Using Regularization-Based Incremental Learning. - Chi-Chang Lee; Yu-Chen Lin 003; Hsuan-Tien Lin; Hsin-Min Wang; Yu Tsao 001 (INTERSPEECH, 2020)

Benchmarking Tropical Cyclone Rapid Intensification with Satellite Images and Attention-Based Deep Models. - Ching-Yuan Bai; Buo-Fu Chen; Hsuan-Tien Lin (ECML/PKDD (4), 2020)

Improving Unsupervised Domain Adaptation with Representative Selection Techniques. - I-Ting Chen; Hsuan-Tien Lin (IAL@PKDD/ECML, 2020)

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation. - Yu-An Chung; Shao-Wen Yang; Hsuan-Tien Lin (TAAI, 2020)

Cost Learning Network for Imbalanced Classification. - Chun-Yi Tu; Hsuan-Tien Lin (TAAI, 2020)

360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales. - Ashesh Mishra; Hsuan-Tien Lin (CoRR, 2020)

Active deep Q-learning with demonstration. - Si-An Chen; Voot Tangkaratt; Hsuan-Tien Lin; Masashi Sugiyama (Mach. Learn., 2020)

360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales. - Ashesh; Chu-Song Chen; Hsuan-Tien Lin (BMVC, 2021)

Adaptive and Generative Zero-Shot Learning. - Yu-Ying Chou; Hsuan-Tien Lin; Tyng-Luh Liu (ICLR, 2021)

On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition. - Ching-Yuan Bai; Hsuan-Tien Lin; Colin Raffel; Wendy Chi-wen Kan (KDD, 2021)

A Unified View of cGANs with and without Classifiers. - Si-An Chen; Chun-Liang Li; Hsuan-Tien Lin (NeurIPS, 2021)

Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination. - Ashesh; Buo-Fu Chen; Treng-Shi Huang; Boyo Chen; Chia-Tung Chang; Hsuan-Tien Lin (CoRR, 2021)

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. - Cheng-Yu Hsieh; Wei-I Lin; Miao Xu 001; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (CoRR, 2021)

Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration. - Si-An Chen; Chun-Liang Li; Hsuan-Tien Lin (CoRR, 2021)

Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN. - Si-An Chen; Jie-Jyun Liu; Tsung-Han Yang; Hsuan-Tien Lin; Chih-Jen Lin (NAACL-HLT, 2022)

Improving Clustering Uncertainty-weighted Embeddings for Active Domain Adaptation. - Sheng-Feng Wu; Hsuan-Tien Lin (TAAI, 2022)

Reducing Training Sample Memorization in GANs by Training with Memorization Rejection. - Andrew Bai; Cho-Jui Hsieh; Wendy Chi-wen Kan; Hsuan-Tien Lin (CoRR, 2022)

Semi-Supervised Domain Adaptation with Source Label Adaptation. - Yu-Chu Yu; Hsuan-Tien Lin (CVPR, 2023)

Reduction from Complementary-Label Learning to Probability Estimates. - Wei-I Lin; Hsuan-Tien Lin (PAKDD (2), 2023)

From SMOTE to Mixup for Deep Imbalanced Classification. - Wei-Chao Cheng; Tan-Ha Mai; Hsuan-Tien Lin (TAAI (1), 2023)

CLCIFAR: CIFAR-Derived Benchmark Datasets with Human Annotated Complementary Labels. - Hsiu-Hsuan Wang; Wei-I Lin; Hsuan-Tien Lin (CoRR, 2023)

Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. - Wei-I Lin; Gang Niu 001; Hsuan-Tien Lin; Masashi Sugiyama (CoRR, 2023)

Understanding and Mitigating Spurious Correlations in Text Classification. - Oscar Chew; Kuan-Hao Huang; Kai-Wei Chang; Hsuan-Tien Lin (CoRR, 2023)

Re-Benchmarking Pool-Based Active Learning for Binary Classification. - Po-Yi Lu; Chun-Liang Li; Hsuan-Tien Lin (CoRR, 2023)

Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance. - Paul Kuo-Ming Huang; Si-An Chen; Hsuan-Tien Lin (CoRR, 2023)

Learning key steps to attack deep reinforcement learning agents. - Chien-Min Yu; Ming-Hsin Chen; Hsuan-Tien Lin (Mach. Learn., 2023)

CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference. - Vo Nguyen Le Duy; Hsuan-Tien Lin; Ichiro Takeuchi (AISTATS, 2024)

Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis. - Oscar Chew; Hsuan-Tien Lin; Kai-Wei Chang; Kuan-Hao Huang (EACL (Findings), 2024)

SLIM: Spuriousness Mitigation with Minimal Human Annotations. - Xiwei Xuan; Ziquan Deng; Hsuan-Tien Lin; Kwan-Liu Ma (ECCV (46), 2024)

TableRAG: Million-Token Table Understanding with Language Models. - Si-An Chen; Lesly Miculicich; Julian Eisenschlos; Zifeng Wang 002; Zilong Wang 002; Yanfei Chen; Yasuhisa Fujii; Hsuan-Tien Lin; Chen-Yu Lee; Tomas Pfister (NeurIPS, 2024)

SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective. - Xiwei Xuan; Ziquan Deng; Hsuan-Tien Lin; Zhaodan Kong; Kwan-Liu Ma (XAI4CV, 2024)

Defending Text-to-image Diffusion Models: Surprising Efficacy of Textual Perturbations Against Backdoor Attacks. - Oscar Chew; Po-Yi Lu; Jayden Lin; Hsuan-Tien Lin (CoRR, 2024)

Reducing Source-Private Bias in Extreme Universal Domain Adaptation. - Hung-Chieh Fang; Po-Yi Lu; Hsuan-Tien Lin (CoRR, 2024)

MAX: Masked Autoencoder for X-ray Fluorescence in Geological Investigation. - An-Sheng Lee; Yu-Wen Pao; Hsuan-Tien Lin; Sofia Ya Hsuan Liou (CoRR, 2024)

libcll: an Extendable Python Toolkit for Complementary-Label Learning. - Nai-Xuan Ye; Tan-Ha Mai; Hsiu-Hsuan Wang; Wei-I Lin; Hsuan-Tien Lin (CoRR, 2024)

Preserving Zero-shot Capability in Supervised Fine-tuning for Multi-label Text Classification. - Si-An Chen; Hsuan-Tien Lin; Chih-Jen Lin (NAACL (Findings), 2025)

The Unexplored Potential of Vision-Language Models for Generating Large-Scale Complementary-Label Learning Data. - Tan-Ha Mai; Nai-Xuan Ye; Yu-Wei Kuan; Po-Yi Lu; Hsuan-Tien Lin (PAKDD (5), 2025)

An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets. - Po-Yi Lu; Yi-Jie Cheng; Chun-Liang Li; Hsuan-Tien Lin (Trans. Mach. Learn. Res., 2025)

CLImage: Human-Annotated Datasets for Complementary-Label Learning. - Hsiu-Hsuan Wang; Tan-Ha Mai; Nai-Xuan Ye; Wei-I Lin; Hsuan-Tien Lin (Trans. Mach. Learn. Res., 2025)

Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning. - Hung-Chieh Fang; Hsuan-Tien Lin; Irwin King; Yifei Zhang (CoRR, 2025)