• 沒有找到結果。

Hsuan-Tien Lin

N/A
N/A
Protected

Academic year: 2022

Share "Hsuan-Tien Lin"

Copied!
22
0
0
顯示更多 ( 頁)

全文

(1)

National Taiwan University, Room 314, CSIE Building, htlin@csie.ntu.edu.tw

#1 Roosevelt Rd. Sec. 4, Taipei 106, Taiwan http://www.csie.ntu.edu.tw/~htlin

+886(2)33664888x314 Last Updated: October 25, 2022

C URRENT P OSITIONS

National Taiwan University, Taipei, Taiwan

Professor, Department of Computer Science and Information Engineering

August 2017–present Adjunct Professor, Graduate Institute of Networking and Multimedia

August 2017–present Associate Chair, Department of Computer Science and Information Engineering

August 2020–presentDepartment Mentor, Department of Computer Science and Information Engineering

August 2021–July 2022 Appier, Taipei, Taiwan

Chief Data Science Consultant March 2019–present

P AST P OSITIONS

Appier, Taipei, Taiwan

Chief Data Scientist February 2016–February 2019

Consultant May 2014–January 2016

National Taiwan University, Taipei, Taiwan

Associate Professor, Department of Computer Science and Information Engineering August 2012–July 2017 Adjunct Associate Professor, Graduate Institute of Networking and Multimedia

August 2012–July 2017 Assistant Professor, Department of Computer Science and Information Engineering

August 2008–July 2012 Adjunct Assistant Professor, Graduate Institute of Networking and Multimedia

August 2008–July 2012

E DUCATION

California Institute of Technology, Pasadena, CA, USA

Ph.D. in Computer Science June 2008

M.S. in Computer Science June 2005

National Taiwan University, Taipei, Taiwan

B.S. in Computer Science and Information Engineering June 2001

R ESEARCH I NTERESTS

• Machine Learning Theory: analyzing the generalization performance of learning systems

• Machine Learning Algorithms: improving existing algorithms including boosting, neural networks, support vector machines; proposing more efficient and more effective

(2)

learning algorithms

• Machine Learning Applications: designing practical machine learning solutions that suit specific application needs

B OOK

[1] Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, Learning from Data: A Short Course(AMLBook, March 2012),ISBN: 978-1600490064

R EFEREED C ONFERENCE P APERS

[55] 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)(July 2022), 1987–2000

[54] 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), vol. 34 (December 2021), 27566–27579

[53] 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)(November 2021), 372

[52] 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)(August 2021)

[51] 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)

[50] 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)(November 2020)

[49] 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), Winner of the best paper award (November 2020)

[48] 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)

[47] 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)

(3)

[46] 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)

[45] 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)

[44] 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)

[43] 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)

[42] 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)

[41] 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) (November 2018), 40–45

[40] 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)(August 2018), 90–99

[39] 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)(June 2018), 143–155

[38] 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)

[37] 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)(February 2018), 3239–3246 [36] 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)

(4)

[35] 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)

[34] Hong-Min Chu and Hsuan-Tien Lin, “Can Active Learning Experience Be Transferred?,” in Proceedings of the IEEE International Conference on Data Mining (ICDM)(December 2016), 841–846

[33] 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)(December 2016), 925–930

[32] 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)(September 2016), 1362–1369

[31] 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)(July 2016), 1411–1417

[30] 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) (June 2016), 473–481

[29] 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), vol. 1 (April 2016), 143–155

[28] 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), vol. 1 (April 2016), 115–126 [27] 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), Winner of the best paper award (October 2015), 554–561

[26] Wei-Ning Hsu and Hsuan-Tien Lin, “Active Learning by Learning,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)(January 2015), 2659–2665 [25] 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), vol. 39, JMLR Workshop and Conference Proceedings (November 2014), 344–359

[24] 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), vol. 39, JMLR Workshop and Conference Proceedings (November 2014), 371–386

[23] Chun-Liang Li and Hsuan-Tien Lin, “Condensed Filter Tree for Cost-Sensitive Multi-Label Classification,” in Proceedings of the International Conference on

(5)

Machine Learning (ICML)(June 2014), 423–431

[22] 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)(June 2014), 342–350

[21] 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, vol. 8444, Lecture Notes in Computer Science (May 2014), 122–133

[20] 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)(December 2013), 25–30

[19] 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) (December 2013), 19–24

[18] 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) (December 2013), 13–18

[17] 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), vol. 29, JMLR Workshop and Conference Proceedings (November 2013), 388–403

[16] 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) (December 2012), 1529–1537

[15] 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), vol. 25, JMLR Workshop and Conference Proceedings (November 2012), 221–235

[14] 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)(August 2012), 141–149

[13] 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)

[12] Chen-Wei Hung and Hsuan-Tien Lin, “Multi-label Active Learning with Auxiliary Learner,” in Proceedings of the Asian Conference on Machine Learning (ACML), vol. 20, JMLR Workshop and Conference Proceedings (November 2011), 315–330

(6)

[11] Chun-Sung Ferng and Hsuan-Tien Lin, “Multi-label Classification with Error- correcting Codes,” in Proceedings of the Asian Conference on Machine Learning (ACML), vol. 20, JMLR Workshop and Conference Proceedings (November 2011), 281–295

[10] 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)(November 2011), 390–393 [9] 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)(June 2011), 905–912

[8] 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)(June 2010), 1095–1102

[7] 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)(August 2007), 749–754

[6] 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)(December 2006), 865–872

[5] Hsuan-Tien Lin and Ling Li, “Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice,” in Algorithmic Learning Theory (ALT), vol. 4264, Lecture Notes in Artificial Intelligence (October 2006), 319–333

[4] 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)(November 2005), 761–766

[3] Ling Li, Amrit Pratap, Hsuan-Tien Lin, and Yaser S. Abu-Mostafa, “Improving Generalization by Data Categorization,” in Knowledge Discovery in Databases (PKDD), vol. 3721, Lecture Notes in Computer Science (November 2005), 157–168 [2] 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), vol. 3720, Lecture Notes in Computer Science (October 2005), 242–254

[1] Shuo-Peng Liao, Hsuan-Tien Lin, and Chih-Jen Lin, “A Note on the Decomposition Methods for Support Vector Regression,” in Proceedings of the 2001 International Joint Conference on Neural Networks (IJCNN)(July 2001), 1474–1479

J OURNAL P APERS

[19] Ashesh, Chia-Tung Chang, Buo-Fu Chen, Hsuan-Tien Lin, Boyo Chen, and Treng- Shi Huang, “Accurate and Clear Quantitative Precipitation Nowcasting Based on

(7)

a Deep Learning Model with Consecutive Attention and Rainmap Discrimination,”

Artificial Intelligence for the Earth Systems1, no. 3 (July 2022): 1–19

[18] Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, and Masashi Sugiyama, “Active Deep Q-learning with Demonstration,” Machine Learning 109, nos. 9–10 (September 2020): 1699–1725

[17] Tsung-Yi Pan, Hsuan-Tien Lin, and Hao-Yu Liao, “A Data-Driven Probabilistic Rainfall-Inundation Model for Flash-Flood Warnings,” Water 11, no. 12 (November 2019): 2534

[16] Hong-Min Chu, Kuan-Hao Huang, and Hsuan-Tien Lin, “Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification,” Machine Learning 108, nos. 8–9 (September 2019): 1193–1230

[15] Yu-Lin Tsou and Hsuan-Tien Lin, “Annotation Cost-sensitive Active Learning by Tree Sampling,” Machine Learning 108, no. 5 (May 2019): 785–807

[14] Chih-Kuan Yeh, Cheng-Yu Hsieh, and Hsuan-Tien Lin, “Automatic Bridge Bidding Using Deep Reinforcement Learning,” IEEE Transactions on Games 10, no. 4 (December 2018): 365–377

[13] Kuan-Hao Huang and Hsuan-Tien Lin, “Cost-Sensitive Label Embedding for Multi- Label Classification,” Machine Learning 106, nos. 9–10 (October 2017): 1725–1746 [12] Yu-Ping Wu and Hsuan-Tien Lin, “Progressive k-Labelsets for Cost-Sensitive

Multi-Label Classification,” Machine Learning 106, no. 5 (May 2017): 671–694 [11] Chun-Liang Li, Chun-Sung Ferng, and Hsuan-Tien Lin, “Active Learning Using

Hint Information,” Neural Computation 27, no. 8 (August 2015): 1738–1765

[10] 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,” Extended first-place winner report of KDD Cup 2013 track 1, Journal of Machine Learning Research16, no. 12 (December 2015): 2921–2947

[9] 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,” Extended first-place winner report of KDD Cup 2013 track 2, Journal of Machine Learning Research 15, no. 10 (October 2014):

3037–3064

[8] Yu-Xun Ruan, Hsuan-Tien Lin, and Ming-Feng Tsai, “Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression,” Information Retrieval 17, no. 1 (February 2014): 1–20

(8)

[7] 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 Systems24, no. 11 (November 2013): 1888–1900

[6] Farbound Tai and Hsuan-Tien Lin, “Multilabel Classification with Principal Label Space Transformation,” Neural Computation 24, no. 9 (September 2012): 2508–2542 [5] 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 Multimedia14, no. 4 (August 2012): 1079–1090

[4] Hsuan-Tien Lin and Ling Li, “Reduction from Cost-sensitive Ordinal Ranking to Weighted Binary Classification,” Neural Computation 24, no. 5 (May 2012):

1329–1367

[3] Hsuan-Tien Lin and Ling Li, “Support Vector Machinery for Infinite Ensemble Learning,” Journal of Machine Learning Research 9, no. 2 (February 2008): 285–312 [2] Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng, “A Note on Platt’s Probabilistic Outputs for Support Vector Machines,” Machine Learning 68, no. 3 (August 2007):

267–276

[1] Shuo-Peng Liao, Hsuan-Tien Lin, and Chih-Jen Lin, “A Note on the Decomposition Methods for Support Vector Regression,” Neural Computation 14, no. 6 (June 2002):

1267–1281

R EFEREED W ORKSHOP P APERS

[14] Si-An Chen, Chun-Liang Li, and Hsuan-Tien Lin, “Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration,” in Proceedings of the Workshop on Deep Generative Models and Downstream Applications @ NeurIPS (December 2021)

[13] Chia-You Chen, Hsuan-Tien Lin, Gang Niu, and Masashi Sugiyama, “On the Role of Pre-training for Meta Few-Shot Learning,” in Proceedings of the 5th Workshop on Meta-Learning @ NeurIPS(December 2021)

[12] I-Ting Chen and Hsuan-Tien Lin, “Improving Unsupervised Domain Adaptation with Representative Selection Techniques,” in Proceedings of the Workshop on Interactive Adaptive Learning @ ECML/PKDD(September 2020), 5–21

[11] Ching-Yuan Bai, Buo-Fu Chen, and Hsuan-Tien Lin, “Attention-based Deep Tropical Cyclone Rapid Intensification Prediction,” in Proceedings of the Workshop on Machine Learning for Earth Observation @ ECML/PKDD(September 2019) [10] 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,” in Proceedings of the Workshop on Learning from Limited Labeled Data @ ICLR(May 2019)

[9] 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 Workshop on Multi-output Learning @ ACML(November 2018)

(9)

[8] Chun-Yen Ho and Hsuan-Tien Lin, “Contract Bridge Bidding by Learning,” in Proceedings of the Workshop on Computer Poker and Imperfect Information @ AAAI (January 2015), 30–36

[7] 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, vol. 18, JMLR Workshop and Conference Proceedings, First- place winner report of KDD Cup 2011 track 2 (May 2012), 101–135

[6] 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, vol. 18, JMLR Workshop and Conference Proceedings, First-place winner report of KDD Cup 2011 track 1 (May 2012), 21–60

[5] 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, First-place winner report of KDD Cup 2010 (July 2010) [4] 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)

[3] 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, and Shou-De Lin,

“An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naïve Bayes,” in Proceedings of KDD-Cup 2009 competition, vol. 7, JMLR Workshop and Conference Proceedings, Third-place winner report of KDD Cup 2009 slow track (June 2009), 57–64

[2] Hsuan-Tien Lin and Ling Li, “Combining Ordinal Preferences by Boosting,” in Proceedings of the Preference Learning Workshop in ECML/PKDD ’09(September 2009), 69–83

[1] Hsuan-Tien Lin and Ling Li, “Analysis of SAGE Results with Combined Learning Techniques,” in Proceedings of the ECML/PKDD 2005 Discovery Challenge (November 2005), 102–113

(10)

O THER P APERS

[12] 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)

[11] Wei-I Lin and Hsuan-Tien Lin, Reduction from Complementary-Label Learning to Probability Estimates, technical report (National Taiwan University, October 2022) [10] 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)

[9] 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)

[8] 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)

[7] 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, First- place winner report of KDD Cup 2012 track 2 (National Taiwan University, August 2012)

[6] 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, First-place winner report of the Exploration and Exploitation Challenge @ ICML 2012 phase 1 (National Taiwan University, June 2012)

[5] Hsuan-Tien Lin, Malik Madgon-Ismail, and Yaser S. Abu-Mostafa, Teaching Machine Learning to a Diverse Audience: the Foundation-based Approach, technical report, Presented in the Teaching Machine Learning Workshop @ ICML ’12 (National Taiwan Unversity, June 2012)

[4] 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, Presented in the Workshop of Yahoo! Learning to Rank Challenge @ ICML ’10 (National Taiwan University, June 2010)

[3] Hsuan-Tien Lin, “From Ordinal Ranking to Binary Classification” (PhD diss., California Institute of Technology, 2008)

[2] Hsuan-Tien Lin, “Infinite Ensemble Learning with Support Vector Machines”

(master’s thesis, California Institute of Technology, 2005)

(11)

[1] Hsuan-Tien Lin and Chih-Jen Lin, A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type methods, technical report (National Taiwan University, March 2003)

S OFTWARE

[1] 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)

T ALKS

Learning with Limited Labeled Data

Artificial Intelligence & Data Science Workshop, Hualien, Taiwan, January 2022 Machine Learning for Modern Artificial Intelligence

Artificial Intelligence & Data Science Workshop, Hualien, Taiwan, January 2022 Wistron NeWeb Corporation, Hsinchu, Taiwan, December 2019 Speech Signal Processing Workshop, Taipei, Taiwan June 2019 Institute of Information Science, Academia Sinica, Taipei, Taiwan November 2018 Unbiased Risk Estimators Can Mislead:

A Case Study of Learning with Complementary Labels South Taiwan Statistics Conference, Kaohsiung, Taiwan, October 2021

AI Forum, Taipei (virtual), Taiwan, October 2021

Active Learning by Learning

IEEE Computational Intelligence Society, International Webinar, April 2021 UBC Centre for Artificial Intelligence Decision-making and Action, International

Webinar January 2021

Information Retrieval Workshop, Taipei, Taiwan December 2015

AI Forum, Kaohsiung, Taiwan, June 2015

Label Space Coding for Multi-label Classification

RIKEN Center for Advanced Intelligence Project, Tokyo, Japan August 2019 Taiwan Society for Industrial and Applied Mathematics Annual Meeting, Taipei, Taiwan May 2018 Institute of Statistics, National University of Kaohsiung, Kaohsiung, Taiwan December 2017

Institute of Statistical Science, Academia Sinica, Taipei, Taiwan September 2015

Intel Web Seminar, Taipei, Taiwan February 2015

Taiwan Society for Industrial and Applied Mathematics Annual Meeting, Kaohsiung,

Taiwan, May 2015

Institute of Communications Engineering, National Tsing Hua University, Hsinchu,

Taiwan April 2014

Department of CS, National Chiao Tung University, Hsinchu, Taiwan May 2013 Institute of Information Science, Academia Sinica, Taipei, Taiwan April 2012 Advances in Cost-sensitive Multiclass and Multilabel Classification

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019

Tutorial, Anchorage, AK, USA August 2019

(12)

Machine Learning for Artificial Intelligence in Medicine Applications

Information Technology Office, National Taiwan University, Taipei, Taiwan July 2019 Division of Hematology and Oncology, Chang Gung Memorial Hospital, New Taipei,

Taiwan March 2019

Mathematical Machine Learning for Modern Artificial Intelligence

Taiwan Society for Industrial and Applied Mathematics Annual Meeting (plenary talk),

Hsinchu, Taiwan May 2019

Developing the Learning Potential of Machines

NTU Center for the Advancement of Science Education, Taipei, Taiwan October 2018 Teaching Machine Learning: Foundations, Techniques and Project

Seed AI Instructor Training Camp, Hsinchu, Taiwan September 2018 Active Learning by Bandit Learning

National Center for Theoretical Sciences at NTU, Taipei, Taiwan March 2018 A Symposium on Complex Data Analysis, Hsinchu, Taiwan May 2017 From Big Data to Artificial Intelligence

National Taiwan University EiMBA Program Guest Lecture, Taipei, TaiwanMarch 2018 From Cloud to Artificial Intelligence

CIO Value Institute, Taipei, Taiwan March 2018

Cost-sensitive Classification: Algorithm and Application

NSYSU AI in Data Science Forum, Kaohsiung, Taiwan December 2017 Choices for Smarter AI

PyCon Taiwan (keynote talk), Taipei, Taiwan June 2017 Quick Tour of Machine Learning

Lecture Series of Data Science Enthusiasts in Taiwan, Taipei, Taiwan March 2017 Lecture Series of Data Science Enthusiasts in Taiwan, Taipei, Taiwan December 2015 From Big Data to Artificial Intelligence

IEEE BigData 2016 Taipei Satellite Session (keynote talk), Taipei, Taiwan May 2016 Learning for Big Data

Department of IM, National Taiwan University, Taipei, Taiwan April 2016 Annual Meeting of Data Science Enthusiasts in Taiwan, Taipei, Taiwan August 2015 IEEE BigData 2015 Taipei Satellite Session (keynote talk), Taipei, Taiwan, May 2015 Machine Learning Overviews and Applications

Industrial Technology Research Institute, Hsinchu, Taiwan January 2016 Department of IM, Chang Jung Christian University, Tainan, Taiwan November 2015

Mediatek, Hsinchu, Taiwan April 2015

Marvell Taiwan, Hsinchu, Taiwan May 2014

My RIGHT-O Lab

National Taiwan University New Faculty Orientation, Hsitou, Taiwan, September 2015

(13)

Competition-Based Final Projects for Enhancing Students’ Motivations

National Taiwan University Teaching Series, Taipei, Taiwan, June 2015 Cost-sensitive Multiclass Classification Using One-versus-one Comparisons

Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan December 2014 The 2nd International Conference on the Interface between Statistics and Engineering,

Tainan, Taiwan June 2012

Institute of Information Science, Academia Sinica, Taipei, Taiwan August 2010 Department of EE, National Tsing Hua University, Hsinchu, Taiwan April 2010 Basics of Machine Learning

Big Data and Machine Learning Summer School, Hsinchu, Taiwan, July 2014 Chien-Kuo Senior High School Talented Class, Taipei, Taiwan, November 2013 My Semi-Flipped Machine Learning Class

National Taiwan University Teaching Series, Taipei, Taiwan, December 2013 Cost-sensitive Classification: Algorithms and Advances

Asian Conference on Machine Learning 2013 Tutorial, Canberra, Australia

November 2013 Cost-sensitive Multiclass Classification via Regression

Department of Civil Engineering, National Taiwan University, Taipei, Taiwan

October 2013 Department of Computer Science and Information Engineering, National Central

University, Taoyuan, Taiwan October 2013

Department of Computer Science, National Cheng Chi University, Taipei, Taiwan November 2011 Department of Computer Science and Information Engineering, National Taiwan

Normal University, Taipei, Taiwan December 2010

Research Center for Information Technology Innovation, Academia Sinica, Taipei,

Taiwan May 2010

Feature-aware Label Space Dimension Reduction for Multi-label Classification

AI Forum, Taipei, Taiwan, May 2013

Taiwan-Israel Joint Workshop on Artificial Intelligence, Tainan, Taiwan November 2012 Deepening Learning Starts after Class

National Taiwan University Teaching Series, Taipei, Taiwan, March 2013 Improving Ranking Performance with Cost-sensitive Ordinal Classification via

Regression

Preference Learning Stream @ EURO 2012, Vilnius, Lithuania, July 2012 Research in the Computational Learning Laboratory: Ordinal Ranking, Cost-

sensitive Classification and KDDCup 2011

Plaxie, Taipei, Taiwan November 2011

Solutions and Experiences from KDD Cup 2011: A Linear Ensemble of Individual and Blended Models for Music Rating Prediction

Research Center for Information Technology Innovation, Academia Sinica, Taipei,

Taiwan October 2011

(14)

A Simple Algorithm for Cost-sensitive Classification

Department of CSIE, National Taiwan University, Taipei, Taiwan September 2008 From Ordinal Ranking to Binary Classification

Department of Applied Math, National Dong Hwa University, Hualian, Taiwan

February 2010 Department of CSIE, National Taiwan University of Science and Technology, Taipei,

Taiwan March 2009

Microsoft Research Asia, Beijing, China February 2009 Department of CS, National Tsing Hua University, Hsinchu, Taiwan March 2008 Department of CS, National Chiao Tung University, Hsinchu, Taiwan March 2008 Department of CSIE, National Taiwan University, Taipei, Taiwan March 2008 CS Department, California Institute of Technology, Pasadena, CA, USA March 2008 Automatic Ranking by Extended Binary Classification

Institute of Information Science, Academia Sinica, Taipei, Taiwan March 2007 EE Department, California Institute of Technology, Pasadena, CA, USANovember 2006 Introduction to Support Vector Machines

Speech Processing Laboratory, National Taiwan University, Taipei, Taiwan

November 2005 Infinite Ensemble Learning with Support Vector Machinery

Caltech 2nd Symposium on Vision and Learning,

California Institute of Technology, Pasadena, CA, USA September 2005 Introduction to Boosting and Joint Boosting

Guest Lecture for EE148 Class: “Machine Learning for Computer Vision”,

California Institute of Technology, Pasadena, CA, USA April 2005

T EACHING E XPERIENCE

National Taiwan University on Coursera, Internet

—Mandarin-teaching Massive Open Online Courses Instructor: Machine Learning Techniques

November 2015, December 2014, March 2020–present Instructor: Machine Learning Foundations—Algorithmic Foundations

November 2017–present Instructor: Machine Learning Foundations—Mathematical Foundations

June 2017–present Instructor: Machine Learning Foundations

September 2015, September 2014, November 2013

National Taiwan University, Taipei, Taiwan

—with performance recognized by winning the outstanding teaching award of the university in 2011 and 2021, see Selected Honors

Instructor: Machine Learning Fall 21-22, Spring 18-19, Fall 15-16, Fall 14-15, Fall 13-14, Fall 12-13,

(15)

Fall 11-12, Fall 10-11, Fall 09-10, Fall 08-09

Instructor: Data Structures and Algorithms Spring 20-21 (co-instructor, classes 01 & 02), Spring 19-20 (class 01), Spring 15-16 (guest lecturer, classes 01 & 02), Spring 14-15 (class 01), Spring 13-14 (classes 01 & 02), Spring 12-13 (class 01), Spring 11-12 (class 01), Spring 10-11 (classes 01 & 02)

Instructor: Fundamendal Object-Oriented Programming Spring 20-21, Fall 15-16 Instructor: Machine Learning Foundations

Fall 20-21, Fall 19-20, Fall 18-19, Fall 17-18, Fall 16-17, Fall 17-18

Instructor: Machine Learning Techniques

Fall 20-21, Spring 19-20, Spring 17-18, Spring 16-17

Instructor: Introduction to Information Theory Fall 19-20

Co-instructor: Computer Science and Information Technology

Spring 14-15 (Machine Learning session), Spring 13-14 (Machine Learning session), Fall 12-13 (Machine Learning session), Fall 09-10 (Machine Learning session) Instructor: Object-Oriented Software Design

Spring 12-13, Spring 09-10 (classes 01 & 02), Spring 08-09 (class 02) Co-instructor: Data Mining and Machine Learning: Theory and Practice

Spring 12-13, Spring 11-12, Spring 10-11, Spring 09-10 Academia Sinica, Taipei, Taiwan

Invited Lecturer on Kernel Machines(TIGP program)

Spring 13-14, Spring 12-13, Spring 11-12, Spring 10-11, Spring 09-10, Spring 08-09

O THER E XPERIENCE

California Institute of Technology, Pasadena, CA, USA

Research Assistant to Professor Yaser S. Abu-Mostafa July 2003–July 2008 California Institute of Technology, Pasadena, CA, USA

Teaching Assistant to Professor Yaser S. Abu-Mostafa: Learning Systems(CS156b) Winter 07-08 California Institute of Technology, Pasadena, CA, USA

Teaching Assistant to Professor Erik Winfree: Information and Complexity(CS129b) Winter 06-07 California Institute of Technology, Pasadena, CA, USA

Teaching Assistant to Professor Yaser S. Abu-Mostafa: Information and Complexity

(CS129a) Fall 04-05, Fall 06-07

National Taiwan University, Taipei, Taiwan

Research Assistant to Professor Chih-Jen Lin March 2003–June 2003

(16)

Defense Office, Kinmen, Taiwan

Personnel Officer(Second Lieutenant) July 2001–March 2003 National Taiwan University, Taipei, Taiwan

Research Assistant to Professor Chih-Jen Lin September 2000–June 2001 Websurf Company, Taipei, Taiwan

Project Manager June 2000–May 2001

Professional Technology Temple BBS System (PTT), Taipei, Taiwan

Root and Core Administrator January 1998–May 2001

Attila Company, Taipei, Taiwan

Chief Technology Officer December 1999–October 2000

S ELECTED H ONORS

Distinguished Teaching Award, National Taiwan University 2011, 2021 (the most prestigeous teaching award given to only 1% of the total faculty members in the university with a duration of five years)

Academic Contribution Award,

EECS College of National Taiwan University 2016, 2021 Outstanding Teaching Award, National Taiwan University 2016, 2017, 2018, 2019, 2020 Creative Young Scholar Award,

Foundation for the Advancement of Outstanding Scholarship in Taiwan 2017

Best Paper Award, TAAI 2015 2015

(with H.-J. Yang)

T.-Y. Wu Memorial Award, National Science Council of Taiwan 2013 Outstanding Mentoring Award, National Taiwan University 2013

(the most prestigeous mentoring award in the university)

First Place, Track 2 of KDD Cup 2013 2013

(with C.-L. Li, Y.-C. Su, T.-W. Lin, C.-H. Tsai, W.-C. Chang, K.-H. Huang, T.-M. Kuo, S.-W.

Lin, Y.-S. Lin, Y.-C. Lu, C.-P. Yang, C.-X. Chang, W.-S. Chin, Y.-C. Juan, H.-Y. Tung, J.-P.

Wang, C.-K. Wei, F. Wu, T.-C. Yin, T. Yu, Y. Zhuang, S.-D. Lin and C.-J. Lin)

First Place, Track 1 of KDD Cup 2013 2013

(with W.-S. Chin, Y.-C. Juan, Y. Zhuang, F. Wu, H.-Y. Tung, T. Yu, J.-P. Wang, C.-X. Chang, C.-P. Yang, W.-C. Chang, K.-H. Huang, T.-M. Kuo, S.-W. Lin, Y.-S. Lin, Y.-C. Lu, Y.-C. Su, C.-K. Wei, T.-C. Yin, C.-L. Li, T.-W. Lin, C.-H. Tsai, S.-D. Lin and C.-J. Lin)

First Place, Track 2 of KDD Cup 2012 2012

(with K.-W. Wu, C.-S. Ferng, C.-H. Ho, A.-C. Liang, C.-H. Huang, W.-Y. Shen, J.-Y. Jiang, M.-H. Yang, T.-W. Lin, C.-P. Lee, P.-H. Kung, C.-E. Wang, T.-W. Ku, C.-Y. Ho, Y.-S. Tai, I-K.

Chen, W.-L. Huang, C.-P. Chou, T.-J. Lin, H.-J. Yang, Y.-K. Wang, C.-T. Li and S.-D. Lin)

(17)

K.-T. Li Young Researcher Award, ACM Taipei Chapter and IICM 2012

First Place, Track 2 of KDD Cup 2011 2011

(with T. G. McKenzie, C.-S. Ferng, Y.-N. Chen, C.-L. Li, C.-H. Tsai, K.-W. Wu, Y.-H. Chang, C.-Y. Li, W.-S. Lin, S.-H. Yu, C.-Y. Lin, P.-W. Wang, C.-M. Ni, W.-L. Su, T.-T. Kuo, C.-T. Tsai, P.-L. Chen, R.-B. Chiu, K.-C. Chou, Y.-C. Chou, C.-C. Wang, C.-H. Wu, C.-J. Lin and S.-D.

Lin)

First Place, Track 1 of KDD Cup 2011 2011

(with P.-L. Chen, C.-T. Tsai, Y.-N. Chen, K.-C. Chou, C.-L. Li, C.-H. Tsai, K.-W. Wu, Y.-C.

Chou, C.-Y. Li, W.-S. Lin, S.-H. Yu, R.-B. Chiu, C.-Y. Lin, C.-C. Wang, P.-W. Wang, W.-L. Su, C.-H. Wu, T.-T. Kuo, T. G. McKenzie, Y.-H. Chang, C.-S. Ferng, C.-M. Ni, C.-J. Lin and S.-D.

Lin)

First Place, KDD Cup 2010 2010

(with H.-F. Yu, H.-Y. Lo, H.-P. Hsieh, J.-K. Lou, T. G. McKenzie, J.-W. Chou, P.-H. Chung, C.-H. Ho, C.-F. Chang, Y.-H. Wei, J.-Y. Weng, E.-S. Yan, C.-W. Chang, T.-T. Kuo, Y.-C. Lo, P.-T Chang, C. Po, C.-Y. Wang, Y.-H. Huang, C.-W. Hung, Y.-X. Ruan, Y.-S. Lin, S.-D. Lin and C.-J. Lin; also first place of all student teams)

Third Place, Slow Track of KDD Cup 2009 2009

(with H.-Y. Lo, K.-W. Chang, S.-T. Chen, T.-H. Chiang, C.-S. Ferng, C.-J. Hsieh, Y.-K. Ko, T.-T.

Kuo, H.-C. Lai, K.-Y. Lin, C.-H. Wang, H.-F. Yu, C.-J. Lin and S.-D. Lin)

President’s Award, National Taiwan University 1997–2001 (7 times, i.e., within the top 5% of the class consecutively in all 7 semesters)

Second Prize, Trend Million-Dollar Internet Programming Contest 2000 (with L.-C. Kung, K.-P. Chen, G.-W. Liu, C.-Y. Wu, and J. Lin)

Asia Champion, ACM International Collegiate Programming Contest 1999 (also worldwide 10th prize, with L.-C. Kung and K.-P. Chen)

P ROFESSIONAL A CTIVITIES

Journal Paper Reviewer

• ACM Transactions on Intelligent Systems and Technology

• ACM Transactions on Knowledge Discovery from Data

• Advances in Artificial Neural Systems

• Advances in Operations Research

• Applied Stochastic Models in Business and Industry

• Artificial Intelligence

• Cognitive Computation

• Computational Statistics and Data Analysis

• Data Mining and Knowledge Discovery

• IEEE Computational Intelligence Magazine

(18)

• IEEE Internet of Things Journal

• IEEE Transactions on Cybernetics

• IEEE Transactions on Image Processing

• IEEE Transactions on Information Forensics and Security

• IEEE Transactions on Information Technology in Biomedicine

• IEEE Transactions on Knowledge and Data Engineering

• IEEE Transactions on Neural Networks and Learning Systems

• IEEE Transactions on Pattern Analysis and Machine Intelligence

• IEEE Transactions on Systems, Man and Cybernetics—Part B

• Journal of Artificial Intelligence Research

• Journal of Information Fusion

• Journal of Information Science and Engineering

• Journal of Machine Learning Research

• Knowledge and Information Systems

• Knowledge-Based Systems

• Machine Learning

• Neural Networks

• Neurocomputing Journal

• International Journal of Business Intelligence and Data Mining

• International Journal of Computational Intelligence Research

• International Journal of Computational Vision and Robotics

• International Journal of Electrical Engineering

• International Journal of Remote Sensing

• Information Processing Letters

• Information Sciences

• Science China—Information Sciences

• Sensors

• The Computer Journal

Conference Paper Meta Reviewer: Senior Program Committee (SPC) or Area Chair (AC)

• International Joint Conferences on Artificial Intelligence(IJCAI) SPC 2018, SPC 2019, SPC 2020, AC 2021, SPC 2022

◦ Distinguished Area Chair 2021

• International Conference on Machine Learning(ICML) AC 2020, AC 2021, AC 2022

• International Conference on Learning Representations(ICLR) AC 2021, AC2022

• AAAI Conference on Artificial Intelligence(AAAI) AC 2019, SPC 2021, AC 2022

• Conference on Neural Information Processing Systems(NeurIPS)

AC 2015, AC 2018, AC 2019, AC 2021

(19)

• Asian Conference on Machine Learning(ACML)

SPC 2014, SPC 2015, SPC 2016, SPC 2017, SPC 2018, SPC 2019 Conference Paper Reviewer

• Conference on Uncertainty in Artificial Intelligence(UAI) 2021, 2022

◦ Top Reviewer 2022

◦ Top Reviewer 2021

• Artificial Intelligence and Statistics Conference(AISTATS)

2017, 2018, 2019, 2020, 2022

◦ Top Reviewer 2022

• British Machine Vision Conference (BMVC) 2021

• European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML/PKDD) 2011, 2014, 2015, 2021

• IEEE Conference on Computer Vision and Pattern Recognition(CVPR) 2006, 2021

• Conference on Technologies and Applications of Artificial Intelligence(TAAI) 2004, 2010, 2011, 2013, 2015-2021

• Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD) 2014, 2015, 2016, 2017, 2018, 2019, 2020

• International Conference on Learning Representations(ICLR) 2018, 2019, 2020

• Workshop on Multi-output Learning(MoL) 2019

• ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD) 2014, 2015, 2016, 2017, 2018, 2019

• International Conference on Machine Learning(ICML) 2015, 2016, 2017, 2018, 2019

• AAAI Conference on Artificial Intelligence(AAAI) 2016, 2017, 2018, 2020

• International Conference on Data Mining(ICDM) 2015, 2018

• IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2015, 2016, 2017, 2018

• Conference on Neural Information Processing Systems(NeurIPS)

2007, 2008, 2009, 2010, 2011, 2013, 2014, 2016, 2017

• ACM International Conference on Information and Knowledge Management(CIKM) 2017

• International Joint Conferences on Artificial Intelligence(IJCAI)

2011, 2013, 2015, 2016

• International Conference on Big Data and Smart Computing(BigComp)

2014, 2015, 2016

• International Joint Conferences on Neural Networks(IJCNN) 2016

• ASE International Conference on Social Informatics 2015

• International Workshop on Machine Learning, Optimization and Big Data (MOD) 2015

• International Conference on Neural Information Processing(ICONIP)

2006, 2011, 2013, 2014

(20)

• Asian Conference on Machine Learning(ACML) 2011, 2012, 2013

• International Computer Symposium(ICS) 2012

• Annual Data Mining and Knowledge Discovery Competition(KDDCup) 2011

• Symposium on Cloud and Services Computing(SC2) 2011

• International Symposium on Information Theory and its Applications(ISITA) 2010

• Conference on Empirical Methods in Natural Language Processing(EMNLP) 2009

• IEEE World Congress on Computational Intelligence(WCCI) 2008

• International Conference on Intelligent Sensors, Sensor Network and Information

Processing(ISSNIP) 2005

Editor

• Associate Editor, IEEE Computational Intelligence Magazine(CIM) 2020–present

• Associate Editor, Journal of Information Science and Engineering(JISE) 2013–2015 Proposal Reviewer

• Research Projects of Ministry of Science and Technology of Taiwan(MOST)

– Meta Reviewer 2019, 2020, 2021

– Reviewer 2015, 2016, 2017, 2018

• Academia Collaboration Projects of Industrial Technology Research Institute of Taiwan(ITRI)

2017

• Research Projects of National Science Council of Taiwan(NSC)

2010, 2011, 2013, 2014

• Undergraduate Research Projects of National Science Council of Taiwan(NSC) 2013, 2014 Conference Organizer

• EXPO Co-Chair, International Conference on Machine Learning(ICML) 2021

• Program Co-Chair, Conference on Neural Information Processing Systems(NeurIPS) 2020

• Publicity Co-Chair, Asian Conference on Machine Learning(ACML) 2017

• Program Co-Chair, Conference on Technologies and Applications of Artificial

Intelligence(TAAI) 2014

• Tutorial Co-Chair, Asian Conference on Machine Learning(ACML) 2014

• Registration Co-Chair, Pacific-Asia Conference on Knowledge Discovery and Data

Mining(PAKDD) 2014

• Registration Chair, Conference on Technologies and Applications of Artificial

Intelligence(TAAI) 2013

• Local Arrangement Co-Chair, Symposium on Cloud and Services Computing(SC2) 2011

(21)

Workshop Organizer

• Machine Learning Research in Taiwan: Challenges and Directions @ TAAI 2010 (with Jane Hsu)

Services for Asian Conference on Machine Learning

• Steering Committee Member 2021–present

Services for Taiwanese Association for Artificial Intelligence

• Council Member (11th, 12th, 13th, 14th) February 2015–present

• Secretary General (10th) February 2013–January 2015

• Thesis Award Reviewer 2009, 2012, 2013, 2015

Services Related to Programming Contests

• Host Scientific Committee Member,

International Olympiad in Informatics(IOI) 2014

• Program Committee Member,

National Contest of High School Students on Informatics 2013

• Invited Lecturer,

Training Camp of International Olympiad in Informatics(TOI) 2009, 2010, 2011, 2012

• Program Committee Member,

National Contest for Private Universities(NCPU) 2012

• Program Committee Member,

Cross-strait Programming Contest at NTHU 2009, 2012

• Program Committee Member,

Project of Collegiate Programming: Practice, Training, Contest(PTC2),

Ministry of Education of Taiwan(MOE) 2011-2012

• Program Committee Member,

Asian Regional Site of International Collegiate Programming Contest(ICPC),

National Sun Yat-Sen University 2010

Natioanl Chiao-Tung University 2009

National Taiwan University 2008

• Problem Designer and Reviewer,

Project of Collegiate Programming: Practice, Training, Contest(PTC),

Ministry of Education of Taiwan(MOE) 2009-2010

Other Services

• Organizing Committee Member, National Computer Science Forum 2012

• White Paper Co-author, AI sub-area, National Science Council of Taiwan 2010

M EMBERSHIPS

The Institute of Electrical and Electronics Engineers (IEEE) and Computational Intelligence Society

Association for Computing Machinery (ACM) and Special Interest Group on Knowledge Discovery in Data (SIGKDD)

(22)

Institute of Information and Computing Machinery (IICM) Taiwanese Association for Artificial Intelligence (TAAI)

Alumni Associatio of NTU Computer Science and Information Engineering (Board Member)

參考文獻

相關文件

To build a cost- sensitive DNN for a K-class cost-sensitive classification prob- lem, the proposed framework replaces the layer-wise pretrain- ing step with layer-wise cost

Chiun-Chuan Chen (National Taiwan University, NTU) Jann-Long Chern (National Central University, NCU) Yung-Fu Fang (National Cheng Kung University, NCKU) Yu-Chen Shu (National

hiding details: don’t directly access internal stuff to gain flexibility and avoid misuse.. Java Member Encapsulation:

• check type information very strictly by compiler (as opposed to single-object polymorphism): ensure type safety in JVM. Note: type information erased

On the other hand Chandra and Taniguchi (2001) constructed the optimal estimating function esti- mator (G estimator) for ARCH model based on Godambes optimal estimating function

Retarded Green’s functions in NHEK/CFT correspondence Hidden conformal symmetry and real-time correlators Hidden conformal symmetry and quasi-normal modes Conclusion and

Matrix model recursive formulation of 1/N expansion: all information encoded in spectral curve ⇒ generates topological string amplitudes... This is what we

Asymptotic Series and Borel Transforms Revisited Alien Calculus and the Stokes Automorphism Trans–Series and the Bridge Equations Stokes Constants and Asymptotics.. 4 The Airy

We further want to be able to embed our KK GUTs in string theory, as higher dimensional gauge theories are highly non-renormalisable.. This works beautifully in the heterotic

Hikami proposed a state integral model which gives a topological invariant for hyperbolic 3-manifold.. Saddle Point of

The Liouville CFT on C g,n describes the UV region of the gauge theory, and the Seiberg-Witten (Gaiotto) curve C SW is obtained as a ramified double cover of C g,n ... ...

Normalizable moduli (sets of on‐shell vacua in string theory) Scale

The Hilbert space of an orbifold field theory [6] is decomposed into twisted sectors H g , that are labelled by the conjugacy classes [g] of the orbifold group, in our case

The entire moduli space M can exist in the perturbative regime and its dimension (∼ M 4 ) can be very large if the flavor number M is large, in contrast with the moduli space found

2 Center for Theoretical Sciences and Center for Quantum Science and Engineering, National Taiwan University, Taipei 10617, Taiwan..

2 Center for Theoretical Sciences and Center for Quantum Science and Engineering, National Taiwan University, Taipei 10617, Taiwan..

Machine Learning for Modern Artificial Intelligence.. Hsuan-Tien

Hsuan-Tien Lin (NTU CSIE) Machine Learning Basics

instance method binding: dynamic, depending on run-time

• view from reference: one compatible reference can point to many advanced contents. • view from method: one compatible method “contract”, many different

• manipulating object status (partially done: instance variable assignments).. • deleting objects —[TODO 3]

Hsuan-Tien Lin (NTU CSIE) Machine Learning Techniques 0/23...

Hsuan-Tien Lin (NTU CSIE) Machine Learning Foundations 0/26... The