|
[XY2024b]
|
Xiwei Yuan, Ziquan Deng, Hsuan-Tien Lin, and Kwan-Liu Ma.
SLIM: Spuriousness mitigation with minimal human annotations.
In Proceedings of the European Conference on Computer Vision
(ECCV), October 2024.
[ bib |
software |
arxiv |
pdf ]
|
|
[XY2024a]
|
Xiwei Yuan, Ziquan Deng, Hsuan-Tien Lin, Zhaodan Kong, and Kwan-Liu Ma.
SUNY: A visual interpretation framework for convolutional neural
networks from a necessary and sufficient perspective.
In Proceedings of the third Explainable AI for Computer Vision
Workshop @ CVPR, pages 8371--8376, 2024.
[ bib |
arxiv |
pdf |
appendix ]
|
|
[VD2024]
|
Vo Nguyen Le Duy, Hsuan-Tien Lin, and Ichiro Takeuchi.
CAD-DA: Controllable anomaly detection after domain adaptation by
statistical inference.
In Proceedings of the 27th International Conference on
Artificial Intelligence and Statistics (AISTATS), pages 1828--1836, May
2024.
[ bib |
arxiv |
pdf ]
|
|
[OC2024]
|
Oscar Chew, Hsuan-Tien Lin, Kai-Wei Chang, and Kuan-Hao Huang.
Understanding and mitigating spurious correlations in text
classification with neighborhood analysis.
In Findings: European Chapter of the Association for
Computational Linguistics (EACL), March 2024.
[ bib |
arxiv |
pdf ]
|
|
[WC2023]
|
Wei-Chao Cheng, Tan-Ha Mai, and Hsuan-Tien Lin.
From SMOTE to mixup for deep imbalanced classification.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 75--96, December
2023.
[ bib |
software |
arxiv |
pdf ]
|
|
[MC2023]
|
Ming-Hsin Chen, Si-An Chen, and Hsuan-Tien Lin.
Active reinforcement learning from demonstration in continuous action
spaces, July 2023.
presented in the AI and HCI Workshop @ ICML.
[ bib |
pdf ]
|
|
[PL2023]
|
Po-Yi Lu, Chun-Liang Li, and Hsuan-Tien Lin.
A more robust baseline for active learning by injecting randomness to
uncertainty sampling, July 2023.
presented in the AI and HCI Workshop @ ICML.
[ bib |
pdf ]
|
|
[YY2023]
|
Yu-Chu Yu and Hsuan-Tien Lin.
Semi-supervised domain adaptation with source label adaptation.
In Proceedings of the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition (CVPR), June 2023.
[ bib |
software |
arxiv |
pdf ]
|
|
[WL2023]
|
Wei-I Lin and Hsuan-Tien Lin.
Reduction from complementary-label learning to probability estimates.
In Proceedings of the Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD), May 2023.
winner of the best paper runner-up award.
[ bib |
arxiv |
pdf ]
|
|
[CY2023]
|
Chien-Min Yu, Ming-Hsin Chen, and Hsuan-Tien Lin.
Learning key steps to attack deep reinforcement learning agents.
Machine Learning, 112:1499--1522, March 2023.
also presented in the journal track of ECML '23.
[ bib |
software |
publisher |
pdf ]
|
|
[PH2022]
|
Paul Kuo-Ming Huang, Si-An Chen, and Hsuan-Tien Lin.
Improving conditional score-based generation with calibrated
classification and joint training, December 2022.
presented in the Workshop on Score-Based Methods @ NeurIPS.
[ bib |
pdf ]
|
|
[SW2022]
|
Sheng-Feng Wu and Hsuan-Tien Lin.
Improving clustering uncertainty-weighted embeddings for active
domain adaptation.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), December 2022.
[ bib |
software |
pdf ]
|
|
[AB2022]
|
Andrew Bai, Cho-Jui Hsieh, Wendy Kan, and Hsuan-Tien Lin.
Reducing training sample memorization in GANs by training with
memorization rejection.
Technical report, National Taiwan University and University of
California, Los Angeles, October 2022.
[ bib |
software |
arxiv |
pdf ]
|
|
[A2022]
|
Ashesh, Chia-Tung Chang, Buo-Fu Chen, Hsuan-Tien Lin, Boyo Chen, and Treng-Shi
Huang.
Accurate and clear quantitative precipitation nowcasting based on a
deep learning model with consecutive attention and rainmap discrimination.
Artificial Intelligence for the Earth Systems, 1(3):1--19, July
2022.
[ bib |
software |
publisher ]
|
|
[SC2022]
|
Si-An Chen, Jie-Jyun Liu, Tsung-Han Yang, Hsuan-Tien Lin, and Chih-Jen Lin.
Even the simplest baseline needs careful re-investigation: A case
study on XML-CNN.
In Proceedings of the Conference of the North American Chapter
of the Association for Computational Linguistics (NAACL), pages 1987--2000,
July 2022.
[ bib |
software |
pdf ]
|
|
[SC2021b]
|
Si-An Chen, Chun-Liang Li, and Hsuan-Tien Lin.
Improving model compatibility of generative adversarial networks by
boundary calibration, December 2021.
presented in the Workshop on Deep Generative Models and Downstream
Applications @ NeurIPS.
[ bib |
arxiv |
pdf ]
|
|
[CC2021]
|
Chia-You Chen, Hsuan-Tien Lin, Gang Niu, and Masashi Sugiyama.
On the role of pre-training for meta few-shot learning, December
2021.
presented in the 5th Workshop on Meta-Learning @ NeurIPS.
[ bib |
pdf ]
|
|
[SC2021a]
|
Si-An Chen, Chun-Liang Li, and Hsuan-Tien Lin.
A unified view of cGANs with and without classifiers.
In Advances in Neural Information Processing Systems:
Proceedings of the 2021 Conference (NeurIPS), volume 34, pages 27566--27579,
December 2021.
[ bib |
software |
arxiv |
pdf |
appendix ]
|
|
[A2021]
|
Ashesh, Chu-Song Chen, and Hsuan-Tien Lin.
360-degree gaze estimation in the wild using multiple zoom scales.
In Proceedings of the British Machine Vision Conference (BMVC),
page 372, November 2021.
[ bib |
software |
arxiv |
pdf ]
|
|
[CH2021]
|
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, and Masashi
Sugiyama.
Active refinement for multi-label learning: A pseudo-label approach.
Technical report, National Taiwan University and RIKEN Center for
Advanced Intelligence Project, September 2021.
A shorter version appeared in the Workshop on Learning from Limited
Labeled Data @ ICLR '19.
[ bib |
arxiv |
pdf ]
|
|
[CB2021]
|
Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, and Wendy Chih-wen Kan.
On training sample memorization: Lessons from benchmarking generative
modeling with a large-scale competition.
In Proceedings of the 27th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD), pages 2534--2542, August 2021.
[ bib |
arxiv |
pdf ]
|
|
[YC2021]
|
Yu-Ying Chou, Hsuan-Tien Lin, and Tyng-Luh Liu.
Adaptive and generative zero-shot learning.
In Proceedings of the International Conference on Learning
Representations (ICLR), May 2021.
[ bib |
publisher |
pdf ]
|
|
[CT2020]
|
Chun-Yi Tu and Hsuan-Tien Lin.
Cost learning network for imbalanced classification.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 47--51, November
2020.
[ bib |
pdf ]
|
|
[YChung2020]
|
Yu-An Chung, Shao-Wen Yang, and Hsuan-Tien Lin.
Cost-sensitive deep learning with layer-wise cost estimation.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), November 2020.
winner of the best paper award.
[ bib |
arxiv |
pdf ]
|
|
[MY2020]
|
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber.
Cold-start active learning through self-supervised language modeling.
In Proceedings of the Conference on Empirical Methods in Natural
Language Processing (EMNLP), November 2020.
[ bib |
arxiv |
pdf ]
|
|
[KT2020]
|
Kuen-Han Tsai and Hsuan-Tien Lin.
Learning from label proportions with consistency regularization.
In Proceedings of the Asian Conference on Machine Learning
(ACML), November 2020.
[ bib |
arxiv |
pdf ]
|
|
[CL2020]
|
Chi-Chang Lee, Yu-Chen Lin, Hsuan-Tien Lin, Hsin-Min Wang, and Yu Tsao.
SERIL: Noise adaptive speech enhancement using regularization-based
incremental learning.
In Proceedings of the Conference of the International Speech
Communication Association (INTERSPEECH), October 2020.
[ bib |
arxiv |
pdf ]
|
|
[CB2020]
|
Ching-Yuan Bai, Buo-Fu Chen, and Hsuan-Tien Lin.
Benchmarking tropical cyclone rapid intensification with satellite
images and attention-based deep models.
In Proceedings of the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD),
September 2020.
A preliminary version appeared in the Workshop on Machine Learning
for Earth Observation @ ECML/PKDD '19.
[ bib |
data |
pdf ]
|
|
[IC2020]
|
I-Ting Chen and Hsuan-Tien Lin.
Improving unsupervised domain adaptation with representative
selection techniques, September 2020.
presented in the Workshop on Interactive Adaptive Learning @
ECML/PKDD.
[ bib |
pdf ]
|
|
[SC2020]
|
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, and Masashi Sugiyama.
Active deep Q-learning with demonstration.
Machine Learning, 109(9--10):1699--1725, September 2020.
also presented in the journal track of ECML '20.
[ bib |
publisher |
arxiv |
pdf ]
|
|
[YChou2020]
|
Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, and Masashi Sugiyama.
Unbiased risk estimators can mislead: A case study of learning with
complementary labels.
In Proceedings of the International Conference on Machine
Learning (ICML), July 2020.
[ bib |
arxiv |
pdf ]
|
|
[TP2019]
|
Tsung-Yi Pan, Hsuan-Tien Lin, and Hao-Yu Liao.
A data-driven probabilistic rainfall-inundation model for flash-flood
warnings.
Water, 11(12):2534, November 2019.
[ bib |
publisher ]
|
|
[YY2019]
|
Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, and Hsuan-Tien Lin.
Deep learning with a rethinking structure for multi-label
classification.
In Proceedings of the Asian Conference on Machine Learning
(ACML), November 2019.
[ bib |
pdf ]
|
|
[CB2019]
|
Ching-Yuan Bai, Buo-Fu Chen, and Hsuan-Tien Lin.
Attention-based deep tropical cyclone rapid intensification
prediction, September 2019.
presented in the Workshop on Machine Learning for Earth Observation @
ECML/PKDD.
[ bib |
arxiv |
pdf ]
|
|
[HC2019]
|
Hong-Min Chu, Kuan-Hao Huang, and Hsuan-Tien Lin.
Dynamic principal projection for cost-sensitive online multi-label
classification.
Machine Learning, 108(8--9):1193--1230, September 2019.
also presented in the journal track of ECML '19.
[ bib |
publisher |
arxiv |
pdf ]
|
|
[CH2019]
|
Cheng-Yu Hsieh, Miao Xu, Gang Niu, Hsuan-Tien Lin, and Masashi Sugiyama.
A pseudo-label method for coarse-to-fine multi-label learning with
limited supervision.
May 2019.
presented in the Workshop on Learning from Limited Labeled Data @
ICLR.
[ bib |
publisher |
pdf ]
|
|
[YT2019]
|
Yu-Lin Tsou and Hsuan-Tien Lin.
Annotation cost-sensitive active learning by tree sampling.
Machine Learning, 108(5):785--807, May 2019.
also presented in the journal track of ACML '18.
[ bib |
publisher |
pdf ]
|
|
[BC2019]
|
BuoFu Chen, Boyo Chen, Hsuan-Tien Lin, and Russell L. Elsberry.
Estimating tropical cyclone intensity by satellite imagery utilizing
convolutional neural networks.
Weather and Forecasting, 34(2):447--465, April 2019.
A shorter version appeared in KDD '18.
[ bib |
publisher ]
|
|
[YP2018]
|
Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, and Edward Y. Chang.
REFUEL: Exploring sparse features in deep reinforcement learning
for fast disease diagnosis.
In Advances in Neural Information Processing Systems:
Proceedings of the 2018 Conference (NeurIPS), December 2018.
[ bib |
pdf |
appendix ]
|
|
[CY2018]
|
Chih-Kuan Yeh, Cheng-Yu Hsieh, and Hsuan-Tien Lin.
Automatic bridge bidding using deep reinforcement learning.
IEEE Transactions on Games, 10(4):365--377, December 2018.
A shorter version appeared in ECAI '16.
[ bib |
software |
pdf ]
|
|
[HC2018]
|
Hsien-Chun Chiu and Hsuan-Tien Lin.
Multi-label classification with feature-aware cost-sensitive label
embedding.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 40--45, November
2018.
[ bib |
pdf ]
|
|
[YY2018b]
|
Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, and Hsuan-Tien Lin.
Deep learning with a rethinking structure for multi-label
classification.
November 2018.
presented in the Workshop on Multi-output Learning @ ACML.
[ bib |
pdf ]
|
|
[BC2018]
|
Boyo Chen, Buo-Fu Chen, and Hsuan-Tien Lin.
Rotation-blended CNNs on a new open dataset for tropical cyclone
image-to-intensity regression.
In Proceedings of the 24th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD), pages 90--99, August 2018.
[ bib |
data |
pdf ]
|
|
[YY2018a]
|
Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, and Hsuan-Tien Lin.
Cost-sensitive reference pair encoding for multi-label learning.
In Proceedings of the Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD), pages 143--155, June 2018.
[ bib |
software |
arxiv |
pdf ]
|
|
[YS2018]
|
Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, and Min Sun.
Compatibility family learning for item recommendation and generation.
In Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), February 2018.
[ bib |
arxiv |
pdf ]
|
|
[CH2018]
|
Cheng-Yu Hsieh, Yi-An Lin, and Hsuan-Tien Lin.
A deep model with local surrogate loss for general cost-sensitive
multi-label learning.
In Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), pages 3239--3246, February 2018.
[ bib |
pdf ]
|
|
[KL2017]
|
Kuo-Hsuan Lo and Hsuan-Tien Lin.
Cost-sensitive encoding for label space dimension reduction
algorithms on multi-label classification.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), December 2017.
[ bib |
pdf ]
|
|
[TJ2017]
|
Te-Kang Jan, Da-Wei Wang, Chi-Hung Lin, and Hsuan-Tien Lin.
Soft methodology for cost-and-error sensitive classification.
Technical report, National Taiwan University and Academia Sinica,
October 2017.
A shorter version appeared in KDD '12.
[ bib |
arxiv |
pdf ]
|
|
[YL2017]
|
Yi-An Lin and Hsuan-Tien Lin.
Cyclic classifier chain for cost-sensitive multilabel classification.
In Proceedings of the IEEE International Conference on Data
Science and Advanced Analytics (DSAA), October 2017.
[ bib |
pdf ]
|
|
[YY2017]
|
Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, and
Hsuan-Tien Lin.
libact: Pool-based active learning in python.
Technical report, National Taiwan University, October 2017.
[ bib |
software |
arxiv |
pdf ]
|
|
[KH2017]
|
Kuan-Hao Huang and Hsuan-Tien Lin.
Cost-sensitive label embedding for multi-label classification.
Machine Learning, 106(9--10):1725--1746, October 2017.
also presented in the journal track of ECML '17.
[ bib |
arxiv |
pdf ]
|
|
[WS2017]
|
Wei-Yuan Shen and Hsuan-Tien Lin.
Active sampling of pairs and points for large-scale linear bipartite
ranking.
Technical report, National Taiwan University, August 2017.
A shorter version appeared in ACML '13.
[ bib |
arxiv |
pdf ]
|
|
[YW2017]
|
Yu-Ping Wu and Hsuan-Tien Lin.
Progressive k-labelsets for cost-sensitive multi-label
classification.
Machine Learning, 106(5):671--694, May 2017.
also presented in the journal track of ACML '16.
[ bib |
publisher |
pdf ]
|
|
[HC2016]
|
Hong-Min Chu and Hsuan-Tien Lin.
Can active learning experience be transferred?
In Proceedings of the IEEE International Conference on Data
Mining (ICDM), pages 841--846, December 2016.
[ bib |
arxiv |
pdf ]
|
|
[KH2016b]
|
Kuan-Hao Huang and Hsuan-Tien Lin.
A novel uncertainty sampling algorithm for cost-sensitive multiclass
active learning.
In Proceedings of the IEEE International Conference on Data
Mining (ICDM), pages 925--930, December 2016.
[ bib |
pdf ]
|
|
[CY2016]
|
Chih-Kuan Yeh and Hsuan-Tien Lin.
Automatic bridge bidding using deep reinforcement learning.
In Proceedings of the 22nd European Conference on Artificial
Intelligence (ECAI), pages 1362--1369, September 2016.
[ bib |
arxiv |
pdf ]
|
|
[YC2016]
|
Yu-An Chung, Hsuan-Tien Lin, and Shao-Wen Yang.
Cost-aware pre-training for multiclass cost-sensitive deep learning.
In Proceedings of the 25th International Joint Conference on
Artificial Intelligence (IJCAI), pages 1411--1417, July 2016.
[ bib |
software |
arxiv |
pdf ]
|
|
[CL2016]
|
Chun-Liang Li, Hsuan-Tien Lin, and Chi-Jen Lu.
Rivalry of two families of algorithms for memory-restricted streaming
pca.
In Proceedings of the 19th International Conference on
Artificial Intelligence and Statistics (AISTATS), pages 473--481, June 2016.
[ bib |
arxiv |
pdf |
appendix ]
|
|
[KH2016a]
|
Kuan-Hao Huang and Hsuan-Tien Lin.
Linear upper confidence bound algorithm for contextual bandit problem
with piled rewards.
In Proceedings of the Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD), volume 1, pages 143--155, April 2016.
[ bib |
pdf ]
|
|
[SY2016]
|
Sheng-Chi You and Hsuan-Tien Lin.
A simple unlearning framework for online learning under concept
drifts.
In Proceedings of the Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD), volume 1, pages 115--126, April 2016.
[ bib |
pdf ]
|
|
[CL2015b]
|
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, and Chih-Jen Lin.
Combination of feature engineering and ranking models for
paper-author identification in KDD Cup 2013.
Journal of Machine Learning Research, 16(12):2921--2947,
December 2015.
extended first-place winner report of KDD Cup 2013 track 1.
[ bib |
pdf ]
|
|
[HY2015]
|
Han-Jay Yang and Hsuan-Tien Lin.
A practical divide-and-conquer approach for preference-based learning
to rank.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 554--561, October
2015.
winner of the best paper award.
[ bib |
pdf ]
|
|
[CL2015a]
|
Chun-Liang Li, Chun-Sung Ferng, and Hsuan-Tien Lin.
Active learning using hint information.
Neural Computation, 27(8):1738--1765, August 2015.
A shorter version appeared in ACML '12.
[ bib |
pdf ]
|
|
[CH2015]
|
Chun-Yen Ho and Hsuan-Tien Lin.
Contract bridge bidding by learning.
In Proceedings of the Workshop on Computer Poker and Imperfect
Information @ AAAI, pages 30--36, January 2015.
[ bib |
pdf ]
|
|
[WH2015]
|
Wei-Ning Hsu and Hsuan-Tien Lin.
Active learning by learning.
In Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), pages 2659--2665, January 2015.
[ bib |
pdf ]
|
|
[KC2014]
|
Ku-Chun Chou, Chao-Kai Chiang, Hsuan-Tien Lin, and Chi-Jen Lu.
Pseudo-reward algorithms for contextual bandits with linear payoff
functions.
In Proceedings of the Asian Conference on Machine Learning
(ACML), volume 39 of JMLR Workshop and Conference Proceedings, pages
344--359, November 2014.
[ bib |
pdf ]
|
|
[HL2014]
|
Hsuan-Tien Lin.
Reduction from cost-sensitive multiclass classification to
one-versus-one binary classification.
In Proceedings of the Asian Conference on Machine Learning
(ACML), volume 39 of JMLR Workshop and Conference Proceedings, pages
371--386, November 2014.
[ bib |
pdf ]
|
|
[WC2014]
|
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, and Chih-Jen Lin.
Effective string processing and matching for author disambiguation.
Journal of Machine Learning Research, 15(10):3037--3064,
October 2014.
extended first-place winner report of KDD Cup 2013 track 2.
[ bib |
pdf ]
|
|
[CL2014b]
|
Chun-Liang Li and Hsuan-Tien Lin.
Condensed filter tree for cost-sensitive multi-label classification.
In Proceedings of the International Conference on Machine
Learning (ICML), pages 423--431, June 2014.
[ bib |
pdf |
appendix ]
|
|
[SC2014]
|
Shang-Tse Chen, Hsuan-Tien Lin, and Chi-Jen Lu.
Boosting with online binary learners for the multiclass bandit
problem.
In Proceedings of the International Conference on Machine
Learning (ICML), pages 342--350, June 2014.
[ bib |
pdf ]
|
|
[YC2014]
|
Yu-Cheng Chou and Hsuan-Tien Lin.
Machine learning approaches for interactive verification.
In Proceedings of the Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD), Part II, volume 8444 of Lecture
Notes in Computer Science, pages 122--133, May 2014.
[ bib |
pdf ]
|
|
[YR2014]
|
Yu-Xun Ruan, Hsuan-Tien Lin, and Ming-Feng Tsai.
Improving ranking performance with cost-sensitive ordinal
classification via regression.
Information Retrieval, 17(1):1--20, February 2014.
[ bib |
pdf ]
|
|
[KL2013]
|
Ken-Yi Lin, Te-Kang Jan, and Hsuan-Tien Lin.
Data selection techniques for large-scale RankSVM.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 25--30, December
2013.
[ bib |
pdf ]
|
|
[YC2013]
|
Ya-Hsuan Chang and Hsuan-Tien Lin.
Pairwise regression with upper confidence bound for contextual bandit
with multiple actions.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 19--24, December
2013.
[ bib |
pdf ]
|
|
[PC2013]
|
Po-Lung Chen and Hsuan-Tien Lin.
Active learning for multiclass cost-sensitive classification using
probabilistic models.
In Proceedings of the Conference on Technologies and
Applications for Artificial Intelligence (TAAI), pages 13--18, December
2013.
[ bib |
pdf ]
|
|
[WS2013]
|
Wei-Yuan Shen and Hsuan-Tien Lin.
Active sampling of pairs and points for large-scale linear bipartite
ranking.
In Proceedings of the Asian Conference on Machine Learning
(ACML), volume 29 of JMLR Workshop and Conference Proceedings, pages
388--403, November 2013.
[ bib |
pdf ]
|
|
[CF2013]
|
Chun-Sung Ferng and Hsuan-Tien Lin.
Multilabel classification using error-correcting codes of hard or
soft bits.
IEEE Transactions on Neural Networks and Learning Systems,
24(11):1888--1900, November 2013.
A shorter version appeared in ACML '11.
[ bib |
pdf ]
|
|
[YC2012]
|
Yao-Nan Chen and Hsuan-Tien Lin.
Feature-aware label space dimension reduction for multi-label
classification.
In Advances in Neural Information Processing Systems:
Proceedings of the 2012 Conference (NeurIPS), pages 1529--1537, December
2012.
[ bib |
pdf ]
|
|
[CL2012]
|
Chun-Liang Li, Chun-Sung Ferng, and Hsuan-Tien Lin.
Active learning with hinted support vector machine.
In Proceedings of the Asian Conference on Machine Learning
(ACML), volume 25 of JMLR Workshop and Conference Proceedings, pages
221--235, November 2012.
[ bib |
pdf ]
|
|
[FT2012]
|
Farbound Tai and Hsuan-Tien Lin.
Multilabel classification with principal label space transformation.
Neural Computation, 24(9):2508--2542, September 2012.
A preliminary version (under a mis-spelled title) appeared in the MLD
Workshop @ ICML '10.
[ bib |
pdf ]
|
|
[YK2012]
|
Yin-Hsi Kuo, Wen-Huang Cheng, Hsuan-Tien Lin, and Winston H. Hsu.
Unsupervised semantic feature discovery for image object retrieval
and tag refinement.
IEEE Transactions on Multimedia, 14(4):1079--1090, August 2012.
[ bib |
pdf ]
|
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[TJ2012]
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Te-Kang Jan, Da-Wei Wang, Chi-Hung Lin, and Hsuan-Tien Lin.
A simple methodology of soft cost-sensitive classification.
In Proceedings of the 18th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD), pages 141--149, August 2012.
[ bib |
pdf ]
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[KW2012]
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Kuan-Wei Wu, Chun-Sung Ferng, Chia-Hua Ho, An-Chun Liang, Chun-Heng Huang,
Wei-Yuan Shen, Jyun-Yu Jiang, Ming-Hao Yang, Ting-Wei Lin, Ching-Pei Lee,
Perng-Hwa Kung, Chin-En Wang, Ting-Wei Ku, Chun-Yen Ho, Yi-Shu Tai, I-Kuei
Chen, Wei-Lun Huang, Che-Ping Chou, Tse-Ju Lin, Han-Jay Yang, Yen-Kai Wang,
Cheng-Te Li, Shou-De Lin, and Hsuan-Tien Lin.
A two-stage ensemble of diverse models for advertisement ranking in
KDD Cup 2012.
Technical report, National Taiwan University, August 2012.
First-place winner report of KDD Cup 2012 track 2.
[ bib |
pdf ]
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[KC2012]
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Ku-Chun Chou and Hsuan-Tien Lin.
Balancing between estimated reward and uncertainty during news
article recommendation for ICML 2012 exploration and exploitation
challenge.
Technical report, National Taiwan University, June 2012.
First-place winner report of the Exploration and Exploitation
Challenge @ ICML 2012 phase 1.
[ bib |
pdf ]
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[HL2012b]
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Hsuan-Tien Lin, Malik Madgon-Ismail, and Yaser S. Abu-Mostafa.
Teaching machine learning to a diverse audience: the foundation-based
approach.
Technical report, National Taiwan Unversity, June 2012.
presented in the Teaching Machine Learning Workshop @ ICML '12.
[ bib |
pdf ]
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[SC2012]
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Shang-Tse Chen, Hsuan-Tien Lin, and Chi-Jen Lu.
An online boosting algorithm with theoretical justifications.
In Proceedings of the International Conference on Machine
Learning (ICML), June 2012.
[ bib |
pdf ]
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[HL2012a]
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Hsuan-Tien Lin and Ling Li.
Reduction from cost-sensitive ordinal ranking to weighted binary
classification.
Neural Computation, 24(5):1329--1367, May 2012.
Some preliminary parts appeared in NeurIPS '06 and PL Workshop @
ECML/PKDD '09.
[ bib |
pdf ]
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[TM2012]
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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, and Shou-De Lin.
Novel models and ensemble techniques to discriminate favorite items
from unrated ones for personalized music recommendation.
In Proceedings of the KDD Cup 2011 Workshop, volume 18 of
JMLR Workshop and Conference Proceedings, pages 101--135, May 2012.
First-place winner report of KDD Cup 2011 track 2.
[ bib |
pdf ]
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[PC2012]
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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, and Shou-De Lin.
A linear ensemble of individual and blended models for music rating
prediction.
In Proceedings of the KDD Cup 2011 Workshop, volume 18 of
JMLR Workshop and Conference Proceedings, pages 21--60, May 2012.
First-place winner report of KDD Cup 2011 track 1.
[ bib |
pdf ]
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[YA2012]
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Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin.
Learning from Data: A Short Course.
AMLBook, March 2012.
[ bib |
http ]
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[CH2011]
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Chen-Wei Hung and Hsuan-Tien Lin.
Multi-label active learning with auxiliary learner.
In Proceedings of the Asian Conference on Machine Learning
(ACML), volume 20 of JMLR Workshop and Conference Proceedings, pages
315--330, November 2011.
[ bib |
pdf ]
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[CF2011]
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Chun-Sung Ferng and Hsuan-Tien Lin.
Multi-label classification with error-correcting codes.
In Proceedings of the Asian Conference on Machine Learning
(ACML), volume 20 of JMLR Workshop and Conference Proceedings, pages
281--295, November 2011.
[ bib |
pdf ]
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[TJ2011]
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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, and Da-Wei Wang.
Cost-sensitive classification on pathogen species of bacterial
meningitis by Surface Enhanced Raman Scattering.
In Proceedings of the IEEE International Conference on
Bioinformatics and Biomedicine (BIBM), pages 390--393, November 2011.
[ bib |
pdf ]
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[YK2011]
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Yin-Hsi Kuo, Hsuan-Tien Lin, Wen-Huang Cheng, Yi-Hsuan Yang, and Winston H.
Hsu.
Unsupervised auxiliary visual words discovery for large-scale image
object retrieval.
In Proceedings of the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition (CVPR), pages 905--912, June 2011.
[ bib |
pdf ]
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[HL2010]
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Hsuan-Tien Lin.
Cost-sensitive classification: Status and beyond.
Technical report, National Taiwan University, November 2010.
presented in the Workshop on Machine Learning Research in Taiwan:
Challenges and Directions @ TAAI '10.
[ bib |
pdf ]
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[HY2010]
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Hsiang-Fu Yu, Hung-Yi Lo, Hsun-Ping Hsieh, Jing-Kai Lou, Todd G. McKenzie,
Jung-Wei Chou, Po-Han Chung, Chia-Hua Ho, Chun-Fu Chang, Yin-Hsuan Wei,
Jui-Yu Weng, En-Syu Yan, Che-Wei Chang, Tsung-Ting Kuo, Yi-Chen Lo, Po Tzu
Chang, Chieh Po, Chien-Yuan Wang, Yi-Hung Huang, Chen-Wei Hung, Yu-Xun Ruan,
Yu-Shi Lin, Shou-De Lin, Hsuan-Tien Lin, and Chih-Jen Lin.
Feature engineering and classifier ensemble for KDD Cup 2010.
In Proceedings of the KDD Cup 2010 Workshop, July 2010.
First-place winner report of KDD Cup 2010.
[ bib |
pdf ]
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[MT2010]
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Ming-Feng Tsai, Shang-Tse Chen, Yao-Nan Chen, Chun-Sung Ferng, Chia-Hsuan Wang,
Tzay-Yeu Wen, and Hsuan-Tien Lin.
An ensemble ranking solution to the yahoo! learning to rank
challenge.
Technical report, National Taiwan University, June 2010.
presented in the Workshop of Yahoo! Learning to Rank Challenge @ ICML
'10.
[ bib |
pdf ]
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[FT2010a]
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Farbound Tai and Hsuan-Tien Lin.
Multi-label classification with principle label space transformation.
In Proceedings of the 2nd International Workshop on learning
from Multi-Label Data @ ICML '10, June 2010.
[ bib |
pdf ]
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[HT2010]
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Han-Hsing Tu and Hsuan-Tien Lin.
One-sided support vector regression for multiclass cost-sensitive
classification.
In Proceedings of the 27th International Conference on Machine
Learning (ICML), pages 1095--1102, June 2010.
[ bib |
pdf ]
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[HL2008b]
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Hsuan-Tien Lin.
From Ordinal Ranking to Binary Classification.
PhD thesis, California Institute of Technology, June 2008.
[ bib |
pdf ]
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[HL2008a]
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Hsuan-Tien Lin and Ling Li.
Support vector machinery for infinite ensemble learning.
Journal of Machine Learning Research, 9(2):285--312, February
2008.
Some preliminary parts appeared in ECML '05 and ICONIP '05.
[ bib |
pdf ]
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[HL2007]
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Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng.
A note on Platt's probabilistic outputs for support vector
machines.
Machine Learning, 68(3):267--276, August 2007.
[ bib |
pdf ]
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[LL2007]
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Ling Li and Hsuan-Tien Lin.
Optimizing 0/1 loss for perceptrons by random coordinate descent.
In Proceedings of the 2007 International Joint Conference on
Neural Networks (IJCNN), pages 749--754, August 2007.
[ bib |
pdf ]
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[LL2006]
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Ling Li and Hsuan-Tien Lin.
Ordinal regression by extended binary classification.
In Advances in Neural Information Processing Systems :
Proceedings of the 2006 Conference (NeurIPS), pages 865--872, December 2006.
[ bib |
pdf ]
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[HL2006]
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Hsuan-Tien Lin and Ling Li.
Large-margin thresholded ensembles for ordinal regression: Theory and
practice.
In Algorithmic Learning Theory (ALT), volume 4264 of
Lecture Notes in Artificial Intelligence, pages 319--333, October 2006.
[ bib |
pdf ]
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[HL2005d]
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Hsuan-Tien Lin and Ling Li.
Novel distance-based SVM kernels for infinite ensemble learning.
In Proceedings of the 12th International Conference on Neural
Information Processing (ICONIP), pages 761--766, November 2005.
[ bib |
pdf ]
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[HL2005c]
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Hsuan-Tien Lin and Ling Li.
Analysis of SAGE results with combined learning techniques.
In Proceedings of the ECML/PKDD 2005 Discovery Challenge,
pages 102--113, November 2005.
[ bib |
pdf ]
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[LL2005]
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Ling Li, Amrit Pratap, Hsuan-Tien Lin, and Yaser S. Abu-Mostafa.
Improving generalization by data categorization.
In Knowledge Discovery in Databases (PKDD), volume 3721 of
Lecture Notes in Computer Science, pages 157--168, November 2005.
[ bib |
pdf ]
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[HL2005b]
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Hsuan-Tien Lin and Ling Li.
Infinite ensemble learning with support vector machines.
In Machine Learning: Proceedings of the 16th European Conference
on Machine Learning (ECML), volume 3720 of Lecture Notes in Computer
Science, pages 242--254, October 2005.
[ bib |
pdf ]
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[HL2005a]
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Hsuan-Tien Lin.
Infinite ensemble learning with support vector machines.
Master's thesis, California Institute of Technology, June 2005.
[ bib |
pdf ]
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