Enhanced Controllability for IDDQ Test Sets Using Partial Scan. - Tapan J. Chakraborty; Sudipta Bhawmik; Robert Bencivenga; Chih-Jen Lin (DAC, 1991)

Built-In Current Sensor for IDDQ Test in CMOS. - Ching-Wen Hsue; Chih-Jen Lin (ITC, 1993)

PSBIST: A Partial-Scan Based Built-In Self-Test Scheme. - Chih-Jen Lin; Yervant Zorian; Sudipta Bhawmik (ITC, 1993)

Timing-Driven Test Point Insertion for Full-Scan and Partial-Scan BIST. - Kwang-Ting Cheng; Chih-Jen Lin (ITC, 1995)

Integration of partial scan and built-in self-test. - Chih-Jen Lin; Yervant Zorian; Sudipta Bhawmik (J. Electron. Test., 1995)

A Hybrid Algorithm for Test Point Selection for Scan-Based BIST. - Huan-Chih Tsai; Kwang-Ting Cheng; Chih-Jen Lin; Sudipta Bhawmik (DAC, 1997)

An Unconstrained Convex Programming Approach to Linear Semi-Infinite Programming. - Chih-Jen Lin; Shu-Cherng Fang; Soon-Yi Wu (SIAM J. Optim., 1998)

Efficient test-point selection for scan-based BIST. - Huan-Chih Tsai; Kwang-Ting Cheng; Chih-Jen Lin; Sudipta Bhawmik (IEEE Trans. Very Large Scale Integr. Syst., 1998)

Newton's Method for Large Bound-Constrained Optimization Problems. - Chih-Jen Lin; Jorge J. Moré (SIAM J. Optim., 1999)

Incomplete Cholesky Factorizations with Limited Memory. - Chih-Jen Lin; Jorge J. Moré (SIAM J. Sci. Comput., 1999)

The analysis of decomposition methods for support vector machines. - Chih-Chung Chang; Chih-Wei Hsu; Chih-Jen Lin (IEEE Trans. Neural Networks Learn. Syst., 2000)

Solving General Capacity Problem by Relaxed Cutting Plane Approach. - Soon-Yi Wu; Shu-Cherng Fang; Chih-Jen Lin (Ann. Oper. Res., 2001)

Optical Coating Designs Using the Family Competition Evolutionary Algorithm. - Jinn-Moon Yang; Jorng-Tzong Horng; Chih-Jen Lin; Cheng-Yan Kao (Evol. Comput., 2001)

Training nu-Support Vector Classifiers: Theory and Algorithms. - Chih-Chung Chang; Chih-Jen Lin (Neural Comput., 2001)

Formulations of Support Vector Machines: A Note from an Optimization Point of View. - Chih-Jen Lin (Neural Comput., 2001)

On the convergence of the decomposition method for support vector machines. - Chih-Jen Lin (IEEE Trans. Neural Networks, 2001)

Analysis of Nonstationary Time Series Using Support Vector Machines. - Ming-Wei Chang; Chih-Jen Lin; Ruby C. Weng (SVM, 2002)

A Simple Decomposition Method for Support Vector Machines. - Chih-Wei Hsu; Chih-Jen Lin (Mach. Learn., 2002)

Training v -Support Vector Regression: Theory and Algorithms. - Chih-Chung Chang; Chih-Jen Lin (Neural Comput., 2002)

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

A comparison of methods for multiclass support vector machines. - Chih-Wei Hsu; Chih-Jen Lin (IEEE Trans. Neural Networks, 2002)

Asymptotic convergence of an SMO algorithm without any assumptions. - Chih-Jen Lin (IEEE Trans. Neural Networks, 2002)

Errata to "On the convergence of the decomposition method for support vector machines". - Chih-Jen Lin (IEEE Trans. Neural Networks, 2002)

Errata to "A comparison of methods for multiclass support vector machines". - Chih-Jen Lin (IEEE Trans. Neural Networks, 2002)

A formal analysis of stopping criteria of decomposition methods for support vector machines. - Chih-Jen Lin (IEEE Trans. Neural Networks, 2002)

Decomposition methods for linear support vector machines. - Kai-Min Chung; Wei-Chun Kao; Tony Sun; Chih-Jen Lin (ICASSP (4), 2003)

Special issue on support vector machines. - Colin Campbell; Chih-Jen Lin; S. Sathiya Keerthi; V. David Sánchez A. (Neurocomputing, 2003)

Radius Margin Bounds for Support Vector Machines with the RBF Kernel. - Kai-Min Chung; Wei-Chun Kao; Chia-Liang Sun; Li-Lun Wang; Chih-Jen Lin (Neural Comput., 2003)

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. - S. Sathiya Keerthi; Chih-Jen Lin (Neural Comput., 2003)

A study on reduced support vector machines. - Kuan-Ming Lin; Chih-Jen Lin (IEEE Trans. Neural Networks, 2003)

A Generalized Bradley-Terry Model: From Group Competition to Individual Skill. - Tzu-Kuo Huang; Chih-Jen Lin; Ruby C. Weng (NIPS, 2004)

Probability Estimates for Multi-class Classification by Pairwise Coupling. - Ting-Fan Wu; Chih-Jen Lin; Ruby C. Weng (J. Mach. Learn. Res., 2004)

Decomposition Methods for Linear Support Vector Machines. - Wei-Chun Kao; Kai-Min Chung; Chia-Liang Sun; Chih-Jen Lin (Neural Comput., 2004)

Analysis of switching dynamics with competing support vector machines. - Ming-Wei Chang; Chih-Jen Lin; Ruby Chiu-Hsing Weng (IEEE Trans. Neural Networks, 2004)

Training Support Vector Machines via SMO-Type Decomposition Methods. - Pai-Hsuen Chen; Rong-En Fan; Chih-Jen Lin (ALT, 2005)

Working Set Selection Using Second Order Information for Training Support Vector Machines. - Rong-En Fan; Pai-Hsuen Chen; Chih-Jen Lin (J. Mach. Learn. Res., 2005)

Leave-One-Out Bounds for Support Vector Regression Model Selection. - Ming-Wei Chang; Chih-Jen Lin (Neural Comput., 2005)

Combining SVMs with Various Feature Selection Strategies. - Yi-Wei Chen; Chih-Jen Lin (Feature Extraction, 2006)

Ranking individuals by group comparisons. - Tzu-Kuo Huang; Chih-Jen Lin; Ruby C. Weng (ICML, 2006)

Generalized Bradley-Terry Models and Multi-Class Probability Estimates. - Tzu-Kuo Huang; Ruby C. Weng; Chih-Jen Lin (J. Mach. Learn. Res., 2006)

A study on SMO-type decomposition methods for support vector machines. - Pai-Hsuen Chen; Rong-En Fan; Chih-Jen Lin (IEEE Trans. Neural Networks, 2006)

Trust region Newton methods for large-scale logistic regression. - Chih-Jen Lin; Ruby C. Weng; S. Sathiya Keerthi (ICML, 2007)

The NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 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 (TRECVID, 2007)

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

Projected Gradient Methods for Nonnegative Matrix Factorization. - Chih-Jen Lin (Neural Comput., 2007)

On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization. - Chih-Jen Lin (IEEE Trans. Neural Networks, 2007)

A dual coordinate descent method for large-scale linear SVM. - Cho-Jui Hsieh; Kai-Wei Chang; Chih-Jen Lin; S. Sathiya Keerthi; S. Sundararajan (ICML, 2008)

A sequential dual method for large scale multi-class linear svms. - S. Sathiya Keerthi; S. Sundararajan; Kai-Wei Chang; Cho-Jui Hsieh; Chih-Jen Lin (KDD, 2008)

Parallel Spectral Clustering. - Yangqiu Song; WenYen Chen; Hongjie Bai; Chih-Jen Lin; Edward Y. Chang (ECML/PKDD (2), 2008)

Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods. - Hsi-Che Liu; Chien-Yu Chen 001; Yu-Ting Liu; Cheng-Bang Chu; Der-Cherng Liang; Lee-Yung Shih; Chih-Jen Lin (J. Biomed. Informatics, 2008)

Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines. - Kai-Wei Chang; Cho-Jui Hsieh; Chih-Jen Lin (J. Mach. Learn. Res., 2008)

Feature Ranking Using Linear SVM. - Yin-Wen Chang; Chih-Jen Lin (WCCI Causation and Prediction Challenge, 2008)

LIBLINEAR: A Library for Large Linear Classification. - Rong-En Fan; Kai-Wei Chang; Cho-Jui Hsieh; Xiang-Rui Wang; Chih-Jen Lin (J. Mach. Learn. Res., 2008)

Trust Region Newton Method for Logistic Regression. - Chih-Jen Lin; Ruby C. Weng; S. Sathiya Keerthi (J. Mach. Learn. Res., 2008)

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy. - Fang-Lan Huang; Cho-Jui Hsieh; Kai-Wei Chang; Chih-Jen Lin (ACL/IJCNLP (2), 2009)

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

Designing, Analyzing and Exploiting Stake-Based Social Networks. - Tsung-Ting Kuo; Jung-Jung Yeh; Chih-Jen Lin; Shou-De Lin (ASONAM, 2010)

Active learning strategies using SVMs. - Ming-Hen Tsai; Chia-Hua Ho; Chih-Jen Lin (IJCNN, 2010)

Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. - Yin-Wen Chang; Cho-Jui Hsieh; Kai-Wei Chang; Michael Ringgaard; Chih-Jen Lin (J. Mach. Learn. Res., 2010)

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models - Fang-Lan Huang; Cho-Jui Hsieh; Kai-Wei Chang; Chih-Jen Lin (J. Mach. Learn. Res., 2010)

A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification. - Guo-Xun Yuan; Kai-Wei Chang; Cho-Jui Hsieh; Chih-Jen Lin (J. Mach. Learn. Res., 2010)

A Bayesian Approximation Method for Online Ranking. - Ruby C. Weng; Chih-Jen Lin (J. Mach. Learn. Res., 2011)

Dual coordinate descent methods for logistic regression and maximum entropy models. - Hsiang-Fu Yu; Fang-Lan Huang; Chih-Jen Lin (Mach. Learn., 2011)

Parallel Spectral Clustering in Distributed Systems. - Wen-Yen Chen; Yangqiu Song; Hongjie Bai; Chih-Jen Lin; Edward Y. Chang (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

LIBSVM: A library for support vector machines. - Chih-Chung Chang; Chih-Jen Lin (ACM Trans. Intell. Syst. Technol., 2011)

Experiences and lessons in developing industry-strength machine learning and data mining software. - Chih-Jen Lin (KDD, 2012)

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

Large-scale linear support vector regression. - Chia-Hua Ho; Chih-Jen Lin (J. Mach. Learn. Res., 2012)

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

An Improved GLMNET for L1-regularized Logistic Regression. - Guo-Xun Yuan; Chia-Hua Ho; Chih-Jen Lin (J. Mach. Learn. Res., 2012)

Recent Advances of Large-Scale Linear Classification. - Guo-Xun Yuan; Chia-Hua Ho; Chih-Jen Lin (Proc. IEEE, 2012)

Large Linear Classification When Data Cannot Fit in Memory. - Hsiang-Fu Yu; Cho-Jui Hsieh; Kai-Wei Chang; Chih-Jen Lin (ACM Trans. Knowl. Discov. Data, 2012)

Dense Non-rigid Point-Matching Using Random Projections. - Raffay Hamid; Dennis DeCoste; Chih-Jen Lin (CVPR, 2013)

Large-Scale Video Summarization Using Web-Image Priors. - Aditya Khosla; Raffay Hamid; Chih-Jen Lin; Neel Sundaresan (CVPR, 2013)

A fast parallel SGD for matrix factorization in shared memory systems. - Yong Zhuang; Wei-Sheng Chin; Yu-Chin Juan; Chih-Jen Lin (RecSys, 2013)

A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM. - Ching-Pei Lee; Chih-Jen Lin (Neural Comput., 2013)

Support Vector Machines. - Po-Wei Wang; Chih-Jen Lin (Data Classification: Algorithms and Applications, 2014)

Large-scale logistic regression and linear support vector machines using spark. - Chieh-Yen Lin; Cheng-Hao Tsai; Ching-Pei Lee; Chih-Jen Lin (IEEE BigData, 2014)

Incremental and decremental training for linear classification. - Cheng-Hao Tsai; Chieh-Yen Lin; Chih-Jen Lin (KDD, 2014)

Large-scale Kernel RankSVM. - Tzu-Ming Kuo; Ching-Pei Lee; Chih-Jen Lin (SDM, 2014)

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

Iteration complexity of feasible descent methods for convex optimization. - Po-Wei Wang; Chih-Jen Lin (J. Mach. Learn. Res., 2014)

Large-Scale Linear RankSVM. - Ching-Pei Lee; Chih-Jen Lin (Neural Comput., 2014)

Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes. - Meng-Chieh Yu; Tong Yu 001; Shao-Chen Wang; Chih-Jen Lin; Edward Y. Chang (Proc. VLDB Endow., 2014)

Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems. - Mu-Chu Lee; Wei-Lin Chiang; Chih-Jen Lin (ICDM, 2015)

Warm Start for Parameter Selection of Linear Classifiers. - Bo-Yu Chu; Chia-Hua Ho; Cheng-Hao Tsai; Chieh-Yen Lin; Chih-Jen Lin (KDD, 2015)

A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization. - Wei-Sheng Chin; Yong Zhuang; Yu-Chin Juan; Chih-Jen Lin (PAKDD (1), 2015)

Distributed Newton Methods for Regularized Logistic Regression. - Yong Zhuang; Wei-Sheng Chin; Yu-Chin Juan; Chih-Jen Lin (PAKDD (2), 2015)

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

Subsampled Hessian Newton Methods for Supervised Learning. - Chien-Chih Wang; Chun-Heng Huang; Chih-Jen Lin (Neural Comput., 2015)

A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems. - Wei-Sheng Chin; Yong Zhuang; Yu-Chin Juan; Chih-Jen Lin (ACM Trans. Intell. Syst. Technol., 2015)

Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments. - Wei-Lin Chiang; Mu-Chu Lee; Chih-Jen Lin (KDD, 2016)

Field-aware Factorization Machines for CTR Prediction. - Yu-Chin Juan; Yong Zhuang; Wei-Sheng Chin; Chih-Jen Lin (RecSys, 2016)

Linear and Kernel Classification: When to Use Which? - Hsin-Yuan Huang; Chih-Jen Lin (SDM, 2016)

LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems. - Wei-Sheng Chin; Bo-Wen Yuan; Mengyuan Yang; Yong Zhuang; Yu-Chin Juan; Chih-Jen Lin (J. Mach. Learn. Res., 2016)

A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information. - Hsiang-Fu Yu; Hsin-Yuan Huang; Inderjit S. Dhillon; Chih-Jen Lin (AAAI, 2017)

A Study on Trust Region Update Rules in Newton Methods for Large-scale Linear Classification. - Chih-Yang Hsia; Ya Zhu; Chih-Jen Lin (ACML, 2017)

Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization. - Ching-Pei Lee; Po-Wei Wang; Weizhu Chen; Chih-Jen Lin (SDM, 2017)

Selection of Negative Samples for One-class Matrix Factorization. - Hsiang-Fu Yu; Mikhail Bilenko; Chih-Jen Lin (SDM, 2017)

Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification. - Chih-Yang Hsia; Wei-Lin Chiang; Chih-Jen Lin (ACML, 2018)

Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification. - Yong Zhuang; Yu-Chin Juan; Guo-Xun Yuan; Chih-Jen Lin (CIKM, 2018)

Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification. - Wei-Lin Chiang; Yu-Sheng Li 001; Ching-Pei Lee; Chih-Jen Lin (SDM, 2018)

Distributed Newton Methods for Deep Neural Networks. - Chien-Chih Wang; Kent Loong Tan; Chun-Ting Chen; Yu-Hsiang Lin; S. Sathiya Keerthi; Dhruv Mahajan 001; S. Sundararajan; Chih-Jen Lin (Neural Comput., 2018)

An Efficient Alternating Newton Method for Learning Factorization Machines. - Wei-Sheng Chin; Bo-Wen Yuan; Mengyuan Yang; Chih-Jen Lin (ACM Trans. Intell. Syst. Technol., 2018)

Improving Ad Click Prediction by Considering Non-displayed Events. - Bo-Wen Yuan; Jui-Yang Hsia; Mengyuan Yang; Hong Zhu 003; Chih-Yao Chang; Zhenhua Dong; Chih-Jen Lin (CIKM, 2019)

The Common-directions Method for Regularized Empirical Risk Minimization. - Po-Wei Wang; Ching-Pei Lee; Chih-Jen Lin (J. Mach. Learn. Res., 2019)

AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction. - Chih-Yao Chang; Xing Tang 007; Bo-Wen Yuan; Jui-Yang Hsia; Zhirong Liu; Zhenhua Dong; Xiuqiang He 001; Chih-Jen Lin (MDM, 2020)

Unbiased Ad Click Prediction for Position-aware Advertising Systems. - Bo-Wen Yuan; Yaxu Liu; Jui-Yang Hsia; Zhenhua Dong; Chih-Jen Lin (RecSys, 2020)

Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term. - Chi-Cheng Chiu; Pin-Yen Lin; Chih-Jen Lin (SDM, 2020)

Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD. - Hung-Yi Chou; Pin-Yen Lin; Chih-Jen Lin (SDM, 2020)

An Efficient Newton Method for Extreme Similarity Learning with Nonlinear Embeddings. - Bo-Wen Yuan; Yu-Sheng Li 001; Pengrui Quan; Chih-Jen Lin (CoRR, 2020)

Newton Methods for Convolutional Neural Networks. - Chien-Chih Wang; Kent Loong Tan; Chih-Jen Lin (ACM Trans. Intell. Syst. Technol., 2020)

Introduction to the Special Issue on the Best Papers from KDD 2018. - Hui Xiong; Chih-Jen Lin (ACM Trans. Knowl. Discov. Data, 2020)

Parameter Selection for Linear Support Vector Regression. - Jui-Yang Hsia; Chih-Jen Lin (IEEE Trans. Neural Networks Learn. Syst., 2020)

Parameter Selection: Why We Should Pay More Attention to It. - Jie-Jyun Liu; Tsung-Han Yang; Si-An Chen; Chih-Jen Lin (ACL/IJCNLP (2), 2021)

Limited-memory Common-directions Method With Subsampled Newton Directions for Large-scale Linear Classification. - Jui-Nan Yen; Chih-Jen Lin (ICDM, 2021)

Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings. - Bo-Wen Yuan; Yu-Sheng Li 001; Pengrui Quan; Chih-Jen Lin (KDD, 2021)

A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems. - Giulio Galvan; Matteo Lapucci; Chih-Jen Lin; Marco Sciandrone (J. Mach. Learn. Res., 2021)

On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations. - Li-Chung Lin; Cheng-Hung Liu 001; Chih-Ming Chen 003; Kai-Chin Hsu; I-Feng Wu; Ming-Feng Tsai; Chih-Jen Lin (AAAI, 2022)

Practical Counterfactual Policy Learning for Top-K Recommendations. - Yaxu Liu; Jui-Nan Yen; Bo-Wen Yuan; Rundong Shi; Peng Yan; Chih-Jen Lin (KDD, 2022)

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

Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization. - Ching-Pei Lee; Po-Wei Wang; Chih-Jen Lin (Math. Program. Comput., 2022)

A Study on Truncated Newton Methods for Linear Classification. - Leonardo Galli; Chih-Jen Lin (IEEE Trans. Neural Networks Learn. Syst., 2022)

Linear Classifier: An Often-Forgotten Baseline for Text Classification. - Yu-Chen Lin; Si-An Chen; Jie-Jyun Liu; Chih-Jen Lin (ACL (2), 2023)

On the Thresholding Strategy for Infrequent Labels in Multi-label Classification. - Yu-Jen Lin; Chih-Jen Lin (CIKM, 2023)

On the "Rough Use" of Machine Learning Techniques. - Chih-Jen Lin (SIGIR, 2023)

Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification. - Thanh-Tung Nguyen; Viktor Schlegel; Abhinav Ramesh Kashyap; Stefan Winkler 001; Shao-Syuan Huang; Jie-Jyun Liu; Chih-Jen Lin (CoRR, 2023)

Random Label Forests: An Ensemble Method with Label Subsampling For Extreme Multi-Label Problems. - Sheng-Wei Chen 003; Chih-Jen Lin (EMNLP (Findings), 2024)

Exploring Space Efficiency in a Tree-based Linear Model for Extreme Multi-label Classification. - He-Zhe Lin; Cheng-Hung Liu 001; Chih-Jen Lin (EMNLP, 2024)

One-class Matrix Factorization: Point-Wise Regression-Based or Pair-Wise Ranking-Based? - Sheng-Wei Chen 003; Chih-Jen Lin (RecSys, 2024)

A Step-by-step Introduction to the Implementation of Automatic Differentiation. - Yu-Hsueh Fang; He-Zhe Lin; Jie-Jyun Liu; Chih-Jen Lin (CoRR, 2024)

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

One-Class SVM Probabilistic Outputs. - Zhongyi Que; Chih-Jen Lin (IEEE Trans. Neural Networks Learn. Syst., 2025)