Chih-Jen Lin

ORCID

Publication Activity (10 Years)

Years Active: 1991-2023
Publications (10 Years): 48

Top Venues

Publications

Yu-Chen Lin, Si-An Chen, Jie-Jyun Liu, Chih-Jen Lin
Linear Classifier: An Often-Forgotten Baseline for Text Classification. CoRR (2023)
Yu-Chen Lin, Si-An Chen, Jie-Jyun Liu, Chih-Jen Lin
Linear Classifier: An Often-Forgotten Baseline for Text Classification. ACL (2) (2023)
Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap, Stefan Winkler, Shao-Syuan Huang, Jie-Jyun Liu, Chih-Jen Lin
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification. CoRR (2023)
Chih-Jen Lin
On the "Rough Use" of Machine Learning Techniques. SIGIR (2023)
Yu-Jen Lin, Chih-Jen Lin
On the Thresholding Strategy for Infrequent Labels in Multi-label Classification. CIKM (2023)
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)
Ching-Pei Lee, Po-Wei Wang, Chih-Jen Lin
Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization. Math. Program. Comput. 14 (3) (2022)
Yaxu Liu, Jui-Nan Yen, Bo-Wen Yuan, Rundong Shi, Peng Yan, Chih-Jen Lin
Practical Counterfactual Policy Learning for Top-K Recommendations. KDD (2022)
Leonardo Galli, Chih-Jen Lin
A Study on Truncated Newton Methods for Linear Classification. IEEE Trans. Neural Networks Learn. Syst. 33 (7) (2022)
Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen, Kai-Chin Hsu, I-Feng Wu, Ming-Feng Tsai, Chih-Jen Lin
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations. AAAI (2022)
Jui-Nan Yen, Chih-Jen Lin
Limited-memory Common-directions Method With Subsampled Newton Directions for Large-scale Linear Classification. ICDM (2021)
Bo-Wen Yuan, Yu-Sheng Li, Pengrui Quan, Chih-Jen Lin
Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings. KDD (2021)
Jie-Jyun Liu, Tsung-Han Yang, Si-An Chen, Chih-Jen Lin
Parameter Selection: Why We Should Pay More Attention to It. ACL/IJCNLP (2) (2021)
Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone
A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems. J. Mach. Learn. Res. 22 (2021)
Jie-Jyun Liu, Tsung-Han Yang, Si-An Chen, Chih-Jen Lin
Parameter Selection: Why We Should Pay More Attention to It. CoRR (2021)
Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen, Kai-Chin Hsu, I-Feng Wu, Ming-Feng Tsai, Chih-Jen Lin
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations. CoRR (2021)
Hung-Yi Chou, Pin-Yen Lin, Chih-Jen Lin
Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD. SDM (2020)
Jui-Yang Hsia, Chih-Jen Lin
Parameter Selection for Linear Support Vector Regression. IEEE Trans. Neural Networks Learn. Syst. 31 (12) (2020)
Bo-Wen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin
Unbiased Ad Click Prediction for Position-aware Advertising Systems. RecSys (2020)
Chih-Yao Chang, Xing Tang, Bo-Wen Yuan, Jui-Yang Hsia, Zhirong Liu, Zhenhua Dong, Xiuqiang He, Chih-Jen Lin
AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction. MDM (2020)
Chien-Chih Wang, Kent Loong Tan, Chih-Jen Lin
Newton Methods for Convolutional Neural Networks. ACM Trans. Intell. Syst. Technol. 11 (2) (2020)
Bo-Wen Yuan, Yu-Sheng Li, Pengrui Quan, Chih-Jen Lin
An Efficient Newton Method for Extreme Similarity Learning with Nonlinear Embeddings. CoRR (2020)
Hui Xiong, Chih-Jen Lin
Introduction to the Special Issue on the Best Papers from KDD 2018. ACM Trans. Knowl. Discov. Data 14 (5) (2020)
Chi-Cheng Chiu, Pin-Yen Lin, Chih-Jen Lin
Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term. SDM (2020)
Bo-Wen Yuan, Jui-Yang Hsia, Meng-Yuan Yang, Hong Zhu, Chih-Yao Chang, Zhenhua Dong, Chih-Jen Lin
Improving Ad Click Prediction by Considering Non-displayed Events. CIKM (2019)
Po-Wei Wang, Ching-Pei Lee, Chih-Jen Lin
The Common-directions Method for Regularized Empirical Risk Minimization. J. Mach. Learn. Res. 20 (2019)
Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin
Distributed Newton Methods for Deep Neural Networks. CoRR (2018)
Wei-Lin Chiang, Yu-Sheng Li, Ching-Pei Lee, Chih-Jen Lin
Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification. SDM (2018)
Yong Zhuang, Yu-Chin Juan, Guo-Xun Yuan, Chih-Jen Lin
Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification. CIKM (2018)
Chih-Yang Hsia, Wei-Lin Chiang, Chih-Jen Lin
Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification. ACML (2018)
Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Chih-Jen Lin
An Efficient Alternating Newton Method for Learning Factorization Machines. ACM Trans. Intell. Syst. Technol. 9 (6) (2018)
Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S. Sathiya Keerthi, Dhruv Mahajan, S. Sundararajan, Chih-Jen Lin
Distributed Newton Methods for Deep Neural Networks. Neural Comput. 30 (6) (2018)
Chien-Chih Wang, Kent Loong Tan, Chih-Jen Lin
Newton Methods for Convolutional Neural Networks. CoRR (2018)
Ching-Pei Lee, Po-Wei Wang, Weizhu Chen, Chih-Jen Lin
Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization. SDM (2017)
Hsiang-Fu Yu, Hsin-Yuan Huang, Inderjit S. Dhillon, Chih-Jen Lin
A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information. AAAI (2017)
Hsiang-Fu Yu, Mikhail Bilenko, Chih-Jen Lin
Selection of Negative Samples for One-class Matrix Factorization. SDM (2017)
Chih-Yang Hsia, Ya Zhu, Chih-Jen Lin
A Study on Trust Region Update Rules in Newton Methods for Large-scale Linear Classification. ACML (2017)
Wei-Lin Chiang, Mu-Chu Lee, Chih-Jen Lin
Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments. KDD (2016)
Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin
Field-aware Factorization Machines for CTR Prediction. RecSys (2016)
Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems. J. Mach. Learn. Res. 17 (2016)
Hsin-Yuan Huang, Chih-Jen Lin
Linear and Kernel Classification: When to Use Which? SDM (2016)
Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin
A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization. PAKDD (1) (2015)
Chien-Chih Wang, Chun-Heng Huang, Chih-Jen Lin
Subsampled Hessian Newton Methods for Supervised Learning. Neural Comput. 27 (8) (2015)
Mu-Chu Lee, Wei-Lin Chiang, Chih-Jen Lin
Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems. ICDM (2015)
Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin
Distributed Newton Methods for Regularized Logistic Regression. PAKDD (2) (2015)
Bo-Yu Chu, Chia-Hua Ho, Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin
Warm Start for Parameter Selection of Linear Classifiers. KDD (2015)
Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin
A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems. ACM Trans. Intell. Syst. Technol. 6 (1) (2015)
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 (2015)
Ching-Pei Lee, Chih-Jen Lin
Large-Scale Linear RankSVM. Neural Comput. 26 (4) (2014)
Meng-Chieh Yu, Tong Yu, Shao-Chen Wang, Chih-Jen Lin, Edward Y. Chang
Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes. Proc. VLDB Endow. 7 (13) (2014)
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) (2014)
Cheng-Hao Tsai, Chieh-Yen Lin, Chih-Jen Lin
Incremental and decremental training for linear classification. KDD (2014)
Tzu-Ming Kuo, Ching-Pei Lee, Chih-Jen Lin
Large-scale Kernel RankSVM. SDM (2014)
Po-Wei Wang, Chih-Jen Lin
Iteration complexity of feasible descent methods for convex optimization. J. Mach. Learn. Res. 15 (1) (2014)
Chieh-Yen Lin, Cheng-Hao Tsai, Ching-Pei Lee, Chih-Jen Lin
Large-scale logistic regression and linear support vector machines using spark. IEEE BigData (2014)
Ching-Pei Lee, Chih-Jen Lin
A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM. Neural Comput. 25 (5) (2013)
Raffay Hamid, Dennis DeCoste, Chih-Jen Lin
Dense Non-rigid Point-Matching Using Random Projections. CVPR (2013)
Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan
Large-Scale Video Summarization Using Web-Image Priors. CVPR (2013)
Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin
A fast parallel SGD for matrix factorization in shared memory systems. RecSys (2013)
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)
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)
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Large Linear Classification When Data Cannot Fit in Memory. ACM Trans. Knowl. Discov. Data 5 (4) (2012)
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)
Chia-Hua Ho, Chih-Jen Lin
Large-scale linear support vector regression. J. Mach. Learn. Res. 13 (2012)
Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin
Recent Advances of Large-Scale Linear Classification. Proc. IEEE 100 (9) (2012)
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)
Chih-Jen Lin
Experiences and lessons in developing industry-strength machine learning and data mining software. KDD (2012)
Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin
An Improved GLMNET for L1-regularized Logistic Regression. J. Mach. Learn. Res. 13 (2012)
Chih-Chung Chang, Chih-Jen Lin
LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2 (3) (2011)
Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang
Parallel Spectral Clustering in Distributed Systems. IEEE Trans. Pattern Anal. Mach. Intell. 33 (3) (2011)
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Large Linear Classification When Data Cannot Fit in Memory. IJCAI (2011)
Ruby C. Weng, Chih-Jen Lin
A Bayesian Approximation Method for Online Ranking. J. Mach. Learn. Res. 12 (2011)
Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin
Dual coordinate descent methods for logistic regression and maximum entropy models. Mach. Learn. 85 (1-2) (2011)
Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin
An improved GLMNET for l1-regularized logistic regression. KDD (2011)
Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models J. Mach. Learn. Res. 11 (2010)
Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification. J. Mach. Learn. Res. 11 (2010)
Tsung-Ting Kuo, Jung-Jung Yeh, Chih-Jen Lin, Shou-De Lin
Designing, Analyzing and Exploiting Stake-Based Social Networks. ASONAM (2010)
Ming-Hen Tsai, Chia-Hua Ho, Chih-Jen Lin
Active learning strategies using SVMs. IJCNN (2010)
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Large linear classification when data cannot fit in memory. KDD (2010)
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. J. Mach. Learn. Res. 11 (2010)
Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy. ACL/IJCNLP (2) (2009)
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)
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin
LIBLINEAR: A Library for Large Linear Classification. J. Mach. Learn. Res. 9 (2008)
Yangqiu Song, WenYen Chen, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang
Parallel Spectral Clustering. ECML/PKDD (2) (2008)
Hsi-Che Liu, Chien-Yu Chen, Yu-Ting Liu, Cheng-Bang Chu, Der-Cherng Liang, Lee-Yung Shih, Chih-Jen Lin
Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods. J. Biomed. Informatics 41 (4) (2008)
Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan
A dual coordinate descent method for large-scale linear SVM. ICML (2008)
Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines. J. Mach. Learn. Res. 9 (2008)
S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin
A sequential dual method for large scale multi-class linear svms. KDD (2008)
Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi
Trust Region Newton Method for Logistic Regression. J. Mach. Learn. Res. 9 (2008)
Yin-Wen Chang, Chih-Jen Lin
Feature Ranking Using Linear SVM. WCCI Causation and Prediction Challenge (2008)
Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi
Trust region Newton methods for large-scale logistic regression. ICML (2007)
Hsuan-Tien Lin, Chih-Jen Lin, Ruby C. Weng
A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 68 (3) (2007)
Ming-Fang Weng, Chun-Kang Chen, Yi-Hsuan Yang, Rong-En Fan, Yu-Ting Hsieh, Yung-Yu Chuang, Winston H. Hsu, Chih-Jen Lin
The NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 2007. TRECVID (2007)
Chih-Jen Lin
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization. IEEE Trans. Neural Networks 18 (6) (2007)
Chih-Jen Lin
Projected Gradient Methods for Nonnegative Matrix Factorization. Neural Comput. 19 (10) (2007)
Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin
A study on SMO-type decomposition methods for support vector machines. IEEE Trans. Neural Networks 17 (4) (2006)
Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng
Ranking individuals by group comparisons. ICML (2006)
Tzu-Kuo Huang, Ruby C. Weng, Chih-Jen Lin
Generalized Bradley-Terry Models and Multi-Class Probability Estimates. J. Mach. Learn. Res. 7 (2006)
Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin
Working Set Selection Using Second Order Information for Training Support Vector Machines. J. Mach. Learn. Res. 6 (2005)
Ming-Wei Chang, Chih-Jen Lin
Leave-One-Out Bounds for Support Vector Regression Model Selection. Neural Comput. 17 (5) (2005)

Chih-Jen Lin.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Chih-Jen Lin