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2020 – today
- 2024
[c69]Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi:
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference. AISTATS 2024: 1828-1836- [c68]Oscar Chew, Hsuan-Tien Lin, Kai-Wei Chang, Kuan-Hao Huang:
Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis. EACL (Findings) 2024: 1013-1025 - [c67]Xiwei Xuan
, Ziquan Deng, Hsuan-Tien Lin, Kwan-Liu Ma:
SLIM: Spuriousness Mitigation with Minimal Human Annotations. ECCV (46) 2024: 215-231 - [i41]Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin, Kwan-Liu Ma:
SLIM: Spuriousness Mitigation with Minimal Human Annotations. CoRR abs/2407.05594 (2024) - [i40]Oscar Chew, Po-Yi Lu, Jayden Lin, Hsuan-Tien Lin:
Defending Text-to-image Diffusion Models: Surprising Efficacy of Textual Perturbations Against Backdoor Attacks. CoRR abs/2408.15721 (2024) - [i39]Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister:
TableRAG: Million-Token Table Understanding with Language Models. CoRR abs/2410.04739 (2024) - [i38]Hung-Chieh Fang, Po-Yi Lu, Hsuan-Tien Lin:
Reducing Source-Private Bias in Extreme Universal Domain Adaptation. CoRR abs/2410.11271 (2024) - [i37]An-Sheng Lee, Yu-Wen Pao, Hsuan-Tien Lin, Sofia Ya Hsuan Liou:
MAX: Masked Autoencoder for X-ray Fluorescence in Geological Investigation. CoRR abs/2410.12330 (2024) - 2023
- [j18]Chien-Min Yu, Ming-Hsin Chen, Hsuan-Tien Lin:
Learning key steps to attack deep reinforcement learning agents. Mach. Learn. 112(5): 1499-1522 (2023) - [c66]Yu-Chu Yu, Hsuan-Tien Lin:
Semi-Supervised Domain Adaptation with Source Label Adaptation. CVPR 2023: 24100-24109 - [c65]Wei-I Lin, Hsuan-Tien Lin:
Reduction from Complementary-Label Learning to Probability Estimates. PAKDD (2) 2023: 469-481 - [c64]Wei-Chao Cheng, Tan-Ha Mai, Hsuan-Tien Lin:
From SMOTE to Mixup for Deep Imbalanced Classification. TAAI (1) 2023: 75-96 - [i36]Yu-Chu Yu, Hsuan-Tien Lin:
Semi-Supervised Domain Adaptation with Source Label Adaptation. CoRR abs/2302.02335 (2023) - [i35]Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin, Zhaodan Kong, Kwan-Liu Ma:
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective. CoRR abs/2303.00244 (2023) - [i34]Hsiu-Hsuan Wang, Wei-I Lin, Hsuan-Tien Lin:
CLCIFAR: CIFAR-Derived Benchmark Datasets with Human Annotated Complementary Labels. CoRR abs/2305.08295 (2023) - [i33]Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR abs/2305.08344 (2023) - [i32]Oscar Chew, Kuan-Hao Huang, Kai-Wei Chang, Hsuan-Tien Lin:
Understanding and Mitigating Spurious Correlations in Text Classification. CoRR abs/2305.13654 (2023) - [i31]Po-Yi Lu, Chun-Liang Li, Hsuan-Tien Lin:
Re-Benchmarking Pool-Based Active Learning for Binary Classification. CoRR abs/2306.08954 (2023) - [i30]Paul Kuo-Ming Huang, Si-An Chen, Hsuan-Tien Lin:
Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance. CoRR abs/2307.04081 (2023) - [i29]Wei-Chao Cheng, Tan-Ha Mai, Hsuan-Tien Lin:
From SMOTE to Mixup for Deep Imbalanced Classification. CoRR abs/2308.15457 (2023) - [i28]Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi:
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference. CoRR abs/2310.14608 (2023) - 2022
- [c63]Si-An Chen, Jie-Jyun Liu, Tsung-Han Yang, Hsuan-Tien Lin, Chih-Jen Lin:
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN. NAACL-HLT 2022: 1987-2000 - [c62]Sheng-Feng Wu, Hsuan-Tien Lin:
Improving Clustering Uncertainty-weighted Embeddings for Active Domain Adaptation. TAAI 2022: 18-23 - [i27]Wei-I Lin, Hsuan-Tien Lin:
Reduction from Complementary-Label Learning to Probability Estimates. CoRR abs/2209.09500 (2022) - [i26]Andrew Bai, Cho-Jui Hsieh, Wendy Chi-wen Kan, Hsuan-Tien Lin:
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection. CoRR abs/2210.12231 (2022) - 2021
- [c61]Ashesh, Chu-Song Chen, Hsuan-Tien Lin:
360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales. BMVC 2021: 372 - [c60]Yu-Ying Chou, Hsuan-Tien Lin, Tyng-Luh Liu:
Adaptive and Generative Zero-Shot Learning. ICLR 2021 - [c59]Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, Wendy Chi-wen Kan:
On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition. KDD 2021: 2534-2542 - [c58]Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin:
A Unified View of cGANs with and without Classifiers. NeurIPS 2021: 27566-27579 - [i25]Ashesh, Buo-Fu Chen, Treng-Shi Huang, Boyo Chen, Chia-Tung Chang, Hsuan-Tien Lin:
Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination. CoRR abs/2102.08175 (2021) - [i24]Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, Wendy Chih-wen Kan:
On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition. CoRR abs/2106.03062 (2021) - [i23]Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR abs/2109.14676 (2021) - [i22]Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin:
A Unified View of cGANs with and without Classifiers. CoRR abs/2111.01035 (2021) - [i21]Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin:
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration. CoRR abs/2111.02316 (2021) - 2020
- [j17]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active deep Q-learning with demonstration. Mach. Learn. 109(9-10): 1699-1725 (2020) - [c57]Kuen-Han Tsai, Hsuan-Tien Lin:
Learning from Label Proportions with Consistency Regularization. ACML 2020: 513-528 - [c56]Michelle Yuan, Hsuan-Tien Lin, Jordan L. Boyd-Graber:
Cold-start Active Learning through Self-supervised Language Modeling. EMNLP (1) 2020: 7935-7948 - [c55]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. ICML 2020: 1929-1938 - [c54]Chi-Chang Lee, Yu-Chen Lin, Hsuan-Tien Lin, Hsin-Min Wang, Yu Tsao:
SERIL: Noise Adaptive Speech Enhancement Using Regularization-Based Incremental Learning. INTERSPEECH 2020: 2432-2436 - [c53]I-Ting Chen, Hsuan-Tien Lin:
Improving Unsupervised Domain Adaptation with Representative Selection Techniques. IAL@PKDD/ECML 2020: 5-21 - [c52]Ching-Yuan Bai, Buo-Fu Chen, Hsuan-Tien Lin:
Benchmarking Tropical Cyclone Rapid Intensification with Satellite Images and Attention-Based Deep Models. ECML/PKDD (4) 2020: 497-512 - [c51]Chun-Yi Tu, Hsuan-Tien Lin:
Cost Learning Network for Imbalanced Classification. TAAI 2020: 47-51 - [c50]Yu-An Chung, Shao-Wen Yang, Hsuan-Tien Lin:
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation. TAAI 2020: 108-113 - [e1]Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin:
Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020 [contents] - [i20]Chi-Chang Lee, Yu-Chen Lin, Hsuan-Tien Lin, Hsin-Min Wang, Yu Tsao:
SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning. CoRR abs/2005.11760 (2020) - [i19]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. CoRR abs/2007.02235 (2020) - [i18]Ashesh Mishra, Hsuan-Tien Lin:
360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales. CoRR abs/2009.06924 (2020) - [i17]Michelle Yuan, Hsuan-Tien Lin, Jordan L. Boyd-Graber:
Cold-start Active Learning through Self-supervised Language Modeling. CoRR abs/2010.09535 (2020)
2010 – 2019
- 2019
- [j16]Yu-Lin Tsou, Hsuan-Tien Lin:
Annotation cost-sensitive active learning by tree sampling. Mach. Learn. 108(5): 785-807 (2019) - [j15]Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin:
Dynamic principal projection for cost-sensitive online multi-label classification. Mach. Learn. 108(8-9): 1193-1230 (2019) - [c49]Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin:
Deep Learning with a Rethinking Structure for Multi-label Classification. ACML 2019: 125-140 - [c48]Hsuan-Tien Lin:
Advances in Cost-sensitive Multiclass and Multilabel Classification. KDD 2019: 3187-3188 - [c47]Ching-Yuan Bai, Buo-Fu Chen, Hsuan-Tien Lin:
Attention-based Deep Tropical Cyclone Rapid Intensification Prediction. MACLEAN@PKDD/ECML 2019 - [i16]Ching-Yuan Bai, Buo-Fu Chen, Hsuan-Tien Lin:
Attention-based Deep Tropical Cyclone Rapid Intensification Prediction. CoRR abs/1909.11616 (2019) - [i15]Kuen-Han Tsai, Hsuan-Tien Lin:
Learning from Label Proportions with Consistency Regularization. CoRR abs/1910.13188 (2019) - 2018
- [j14]Chih-Kuan Yeh, Cheng-Yu Hsieh, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. IEEE Trans. Games 10(4): 365-377 (2018) - [c46]Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, Min Sun:
Compatibility Family Learning for Item Recommendation and Generation. AAAI 2018: 2403-2410 - [c45]Cheng-Yu Hsieh, Yi-An Lin, Hsuan-Tien Lin:
A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning. AAAI 2018: 3239-3246 - [c44]Boyo Chen, Buo-Fu Chen, Hsuan-Tien Lin:
Rotation-blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-intensity Regression. KDD 2018: 90-99 - [c43]Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Y. Chang:
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis. NeurIPS 2018: 7333-7342 - [c42]Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, Hsuan-Tien Lin:
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning. PAKDD (1) 2018: 143-155 - [c41]Hsien-Chun Chiu, Hsuan-Tien Lin:
Multi-Label Classification with Feature-Aware Cost-Sensitive Label Embedding. TAAI 2018: 40-45 - [i14]Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin:
Deep Learning with a Rethinking Structure for Multi-label Classification. CoRR abs/1802.01697 (2018) - [i13]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active Deep Q-learning with Demonstration. CoRR abs/1812.02632 (2018) - 2017
- [j13]Yuping Wu, Hsuan-Tien Lin:
Progressive random k-labelsets for cost-sensitive multi-label classification. Mach. Learn. 106(5): 671-694 (2017) - [j12]Kuan-Hao Huang, Hsuan-Tien Lin:
Cost-sensitive label embedding for multi-label classification. Mach. Learn. 106(9-10): 1725-1746 (2017) - [c40]Yi-An Lin, Hsuan-Tien Lin:
Cyclic Classifier Chain for Cost-Sensitive Multilabel Classification. DSAA 2017: 11-20 - [c39]Kuo-Hsuan Lo, Hsuan-Tien Lin:
Cost-Sensitive Encoding for Label Space Dimension Reduction Algorithms on Multi-label Classification. TAAI 2017: 136-141 - [i12]Wei-Yuan Shen, Hsuan-Tien Lin:
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking. CoRR abs/1708.07336 (2017) - [i11]Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, Hsuan-Tien Lin:
libact: Pool-based Active Learning in Python. CoRR abs/1710.00379 (2017) - [i10]Te-Kang Jan, Da-Wei Wang, Chi-Hung Lin, Hsuan-Tien Lin:
Soft Methodology for Cost-and-error Sensitive Classification. CoRR abs/1710.09515 (2017) - [i9]Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin:
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification. CoRR abs/1711.05060 (2017) - [i8]Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, Min Sun:
Compatibility Family Learning for Item Recommendation and Generation. CoRR abs/1712.01262 (2017) - 2016
- [c38]Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu:
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. AISTATS 2016: 473-481 - [c37]Chih-Kuan Yeh, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. ECAI 2016: 1362-1369 - [c36]Hong-Min Chu, Hsuan-Tien Lin:
Can Active Learning Experience Be Transferred? ICDM 2016: 841-846 - [c35]Kuan-Hao Huang, Hsuan-Tien Lin:
A Novel Uncertainty Sampling Algorithm for Cost-Sensitive Multiclass Active Learning. ICDM 2016: 925-930 - [c34]Yu-An Chung, Hsuan-Tien Lin, Shao-Wen Yang:
Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning. IJCAI 2016: 1411-1417 - [c33]Sheng-Chi You, Hsuan-Tien Lin:
A Simple Unlearning Framework for Online Learning Under Concept Drifts. PAKDD (1) 2016: 115-126 - [c32]Kuan-Hao Huang, Hsuan-Tien Lin:
Linear Upper Confidence Bound Algorithm for Contextual Bandit Problem with Piled Rewards. PAKDD (2) 2016: 143-155 - [i7]Kuan-Hao Huang, Hsuan-Tien Lin:
Cost-sensitive Label Embedding for Multi-label Classification. CoRR abs/1603.09048 (2016) - [i6]Chih-Kuan Yeh, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. CoRR abs/1607.03290 (2016) - [i5]Hong-Min Chu, Hsuan-Tien Lin:
Can Active Learning Experience Be Transferred? CoRR abs/1608.00667 (2016) - [i4]Yu-An Chung, Hsuan-Tien Lin:
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation. CoRR abs/1611.05134 (2016) - [i3]Yao-Yuan Yang, Chih-Wei Chang, Hsuan-Tien Lin:
Cost-Sensitive Random Pair Encoding for Multi-Label Classification. CoRR abs/1611.09461 (2016) - 2015
- [j11]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, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013. J. Mach. Learn. Res. 16: 2921-2947 (2015) - [j10]Chun-Liang Li, Chun-Sung Ferng, Hsuan-Tien Lin:
Active Learning Using Hint Information. Neural Comput. 27(8): 1738-1765 (2015) - [c31]Chun-Yen Ho, Hsuan-Tien Lin:
Contract Bridge Bidding by Learning. AAAI Workshop: Computer Poker and Imperfect Information 2015 - [c30]Wei-Ning Hsu, Hsuan-Tien Lin:
Active Learning by Learning. AAAI 2015: 2659-2665 - [c29]Han-Jay Yang, Hsuan-Tien Lin:
A practical divide-and-conquer approach for preference-based learning to rank. TAAI 2015: 554-561 - [i2]Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu:
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. CoRR abs/1506.01490 (2015) - [i1]Yu-An Chung, Hsuan-Tien Lin, Shao-Wen Yang:
Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning. CoRR abs/1511.09337 (2015) - 2014
- [j9]Yu-Xun Ruan, Hsuan-Tien Lin, Ming-Feng Tsai:
Improving ranking performance with cost-sensitive ordinal classification via regression. Inf. Retr. 17(1): 1-20 (2014) - [j8]Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. J. Mach. Learn. Res. 15(1): 3037-3064 (2014) - [c28]Ku-Chun Chou, Hsuan-Tien Lin, Chao-Kai Chiang, Chi-Jen Lu:
Pseudo-reward Algorithms for Contextual Bandits with Linear Payoff Functions. ACML 2014 - [c27]Hsuan-Tien Lin:
Reduction from Cost-Sensitive Multiclass Classification to One-versus-One Binary Classification. ACML 2014 - [c26]Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:
Boosting with Online Binary Learners for the Multiclass Bandit Problem. ICML 2014: 342-350 - [c25]Chun-Liang Li, Hsuan-Tien Lin:
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. ICML 2014: 423-431 - [c24]Yu-Cheng Chou, Hsuan-Tien Lin:
Machine Learning Approaches for Interactive Verification. PAKDD (2) 2014: 122-133 - 2013
- [j7]Chun-Sung Ferng, Hsuan-Tien Lin:
Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits. IEEE Trans. Neural Networks Learn. Syst. 24(11): 1888-1900 (2013) - [c23]Wei-Yuan Shen, Hsuan-Tien Lin:
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking. ACML 2013: 388-403 - [c22]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, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-de Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Combination of feature engineering and ranking models for paper-author identification in KDD Cup 2013. KDD Cup 2013: 2:1-2:7 - [c21]Wei-Sheng Chin, Yu-Chin Juan, Yong Zhuang, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. KDD Cup 2013: 7:1-7:9 - [c20]Po-Lung Chen, Hsuan-Tien Lin:
Active Learning for Multiclass Cost-Sensitive Classification Using Probabilistic Models. TAAI 2013: 13-18 - [c19]Ya-Hsuan Chang, Hsuan-Tien Lin:
Pairwise Regression with Upper Confidence Bound for Contextual Bandit with Multiple Actions. TAAI 2013: 19-24 - [c18]Ken-Yi Lin, Te-Kang Jan, Hsuan-Tien Lin:
Data Selection Techniques for Large-Scale Rank SVM. TAAI 2013: 25-30 - 2012
- [j6]Hsuan-Tien Lin, Ling Li:
Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification. Neural Comput. 24(5): 1329-1367 (2012) - [j5]Farbound Tai, Hsuan-Tien Lin:
Multilabel Classification with Principal Label Space Transformation. Neural Comput. 24(9): 2508-2542 (2012) - [j4]Yin-Hsi Kuo, Wen-Huang Cheng, Hsuan-Tien Lin, Winston H. Hsu:
Unsupervised Semantic Feature Discovery for Image Object Retrieval and Tag Refinement. IEEE Trans. Multim. 14(4): 1079-1090 (2012) - [c17]Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:
An Online Boosting Algorithm with Theoretical Justifications. ICML 2012 - [c16]Te-Kang Jan, Da-Wei Wang, Chi-Hung Lin, Hsuan-Tien Lin:
A simple methodology for soft cost-sensitive classification. KDD 2012: 141-149 - [c15]Yao-Nan Chen, Hsuan-Tien Lin:
Feature-aware Label Space Dimension Reduction for Multi-label Classification. NIPS 2012: 1538-1546 - [c14]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, Chieh-Yen Lin, Chien-Chih Wang, Po-Wei Wang, Wei-Lun Su, Chen-Hung Wu, Tsung-Ting Kuo, Todd G. McKenzie, Ya-Hsuan Chang, Chun-Sung Ferng, Chia-Mau Ni, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin:
A Linear Ensemble of Individual and Blended Models for Music Rating Prediction. KDD Cup 2012: 21-60 - [c13]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, Chia-Mau Ni, Wei-Lun Su, Tsung-Ting Kuo, Chen-Tse Tsai, Po-Lung Chen, Rong-Bing Chiu, Ku-Chun Chou, Yu-Cheng Chou, Chien-Chih Wang, Chen-Hung Wu, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin:
Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation. KDD Cup 2012: 101-135 - [c12]Chun-Liang Li, Chun-Sung Ferng, Hsuan-Tien Lin:
Active Learning with Hinted Support Vector Machine. ACML 2012: 221-235 - 2011
- [c11]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, Yuh-Lin Wang, Chi-Hung Lin, Da-Wei Wang:
Cost-Sensitive Classification on Pathogen Species of Bacterial Meningitis by Surface Enhanced Raman Scattering. BIBM 2011: 390-393 - [c10]Yin-Hsi Kuo, Hsuan-Tien Lin, Wen-Huang Cheng, Yi-Hsuan Yang, Winston H. Hsu:
Unsupervised auxiliary visual words discovery for large-scale image object retrieval. CVPR 2011: 905-912 - [c9]Chun-Sung Ferng, Hsuan-Tien Lin:
Multi-label Classification with Error-correcting Codes. ACML 2011: 281-295 - [c8]Chen-Wei Hung, Hsuan-Tien Lin:
Multi-label Active Learning with Auxiliary Learner. ACML 2011: 315-332 - 2010
- [c7]Han-Hsing Tu, Hsuan-Tien Lin:
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification. ICML 2010: 1095-1102
2000 – 2009
- 2009
- [c6]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, Chih-Jen Lin, Hsuan-Tien Lin, Shou-De Lin:
An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes. KDD Cup 2009: 57-64 - 2008
- [b1]Hsuan-Tien Lin:
From Ordinal Ranking to Binary Classification. California Institute of Technology, USA, 2008 - [j3]Hsuan-Tien Lin, Ling Li:
Support Vector Machinery for Infinite Ensemble Learning. J. Mach. Learn. Res. 9: 285-312 (2008) - 2007
- [j2]Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng:
A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 68(3): 267-276 (2007) - [c5]Ling Li, Hsuan-Tien Lin:
Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent. IJCNN 2007: 749-754 - 2006
- [c4]Hsuan-Tien Lin, Ling Li:
Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice. ALT 2006: 319-333 - [c3]Ling Li, Hsuan-Tien Lin:
Ordinal Regression by Extended Binary Classification. NIPS 2006: 865-872 - 2005
- [c2]Hsuan-Tien Lin, Ling Li:
Infinite Ensemble Learning with Support Vector Machines. ECML 2005: 242-254 - [c1]Ling Li, Amrit Pratap, Hsuan-Tien Lin, Yaser S. Abu-Mostafa:
Improving Generalization by Data Categorization. PKDD 2005: 157-168 - 2002
- [j1]Shuo-Peng Liao, Hsuan-Tien Lin, Chih-Jen Lin:
A Note on the Decomposition Methods for Support Vector Regression. Neural Comput. 14(6): 1267-1281 (2002)
Coauthor Index
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