Chih-Jen Lin

According to our database1, Chih-Jen Lin authored at least 109 papers between 1991 and 2020.

Collaborative distances:

Awards

ACM Fellow

ACM Fellow 2015, "For contributions to the theory and practice of machine learning and data mining.".

IEEE Fellow

IEEE Fellow 2011, "For contributions to support vector machine algorithms and software".

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

Homepages:

On csauthors.net:

Bibliography

2020
Newton Methods for Convolutional Neural Networks.
ACM Trans. Intell. Syst. Technol., 2020

Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD.
Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term.
Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

2019
The Common-directions Method for Regularized Empirical Risk Minimization.
J. Mach. Learn. Res., 2019

Improving Ad Click Prediction by Considering Non-displayed Events.
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

2018
An Efficient Alternating Newton Method for Learning Factorization Machines.
ACM Trans. Intell. Syst. Technol., 2018

Distributed Newton Methods for Deep Neural Networks.
Neural Computation, 2018

Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification.
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification.
Proceedings of The 10th Asian Conference on Machine Learning, 2018

2017
Selection of Negative Samples for One-class Matrix Factorization.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

A Study on Trust Region Update Rules in Newton Methods for Large-scale Linear Classification.
Proceedings of The 9th Asian Conference on Machine Learning, 2017

A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems.
J. Mach. Learn. Res., 2016

Linear and Kernel Classification: When to Use Which?
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Field-aware Factorization Machines for CTR Prediction.
Proceedings of the 10th ACM Conference on Recommender Systems, 2016

Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015
A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems.
ACM Trans. Intell. Syst. Technol., 2015

Subsampled Hessian Newton Methods for Supervised Learning.
Neural Computation, 2015

Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013.
J. Mach. Learn. Res., 2015

Distributed Newton Methods for Regularized Logistic Regression.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

Warm Start for Parameter Selection of Linear Classifiers.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Fast Matrix-Vector Multiplications for Large-Scale Logistic Regression on Shared-Memory Systems.
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

2014
Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes.
Proc. VLDB Endow., 2014

Large-Scale Linear RankSVM.
Neural Computation, 2014

Iteration complexity of feasible descent methods for convex optimization.
J. Mach. Learn. Res., 2014

Effective string processing and matching for author disambiguation.
J. Mach. Learn. Res., 2014

Large-scale Kernel RankSVM.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Incremental and decremental training for linear classification.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Large-scale logistic regression and linear support vector machines using spark.
Proceedings of the 2014 IEEE International Conference on Big Data, 2014

Support Vector Machines.
Proceedings of the Data Classification: Algorithms and Applications, 2014

2013
A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM.
Neural Computation, 2013

A fast parallel SGD for matrix factorization in shared memory systems.
Proceedings of the Seventh ACM Conference on Recommender Systems, 2013

Large-Scale Video Summarization Using Web-Image Priors.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

Dense Non-rigid Point-Matching Using Random Projections.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012
Large Linear Classification When Data Cannot Fit in Memory.
ACM Trans. Knowl. Discov. Data, 2012

Recent Advances of Large-Scale Linear Classification.
Proceedings of the IEEE, 2012

An Improved GLMNET for L1-regularized Logistic Regression.
J. Mach. Learn. Res., 2012

Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation.
Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

Large-scale linear support vector regression.
J. Mach. Learn. Res., 2012


Experiences and lessons in developing industry-strength machine learning and data mining software.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

2011
LIBSVM: A library for support vector machines.
ACM Trans. Intell. Syst. Technol., 2011

Parallel Spectral Clustering in Distributed Systems.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Dual coordinate descent methods for logistic regression and maximum entropy models.
Mach. Learn., 2011

A Bayesian Approximation Method for Online Ranking.
J. Mach. Learn. Res., 2011

2010
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification.
J. Mach. Learn. Res., 2010

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
J. Mach. Learn. Res., 2010

Training and Testing Low-degree Polynomial Data Mappings via Linear SVM.
J. Mach. Learn. Res., 2010

Active learning strategies using SVMs.
Proceedings of the International Joint Conference on Neural Networks, 2010

Designing, Analyzing and Exploiting Stake-Based Social Networks.
Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2010

2009
An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes.
Proceedings of KDD-Cup 2009 competition, Paris, France, June 28, 2009, 2009

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy.
Proceedings of the ACL 2009, 2009

2008
Trust Region Newton Method for Logistic Regression.
J. Mach. Learn. Res., 2008

LIBLINEAR: A Library for Large Linear Classification.
J. Mach. Learn. Res., 2008

Feature Ranking Using Linear SVM.
Proceedings of the Causation and Prediction Challenge at WCCI 2008, 2008

Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines.
J. Mach. Learn. Res., 2008

Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods.
J. Biomed. Informatics, 2008

Parallel Spectral Clustering.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

A sequential dual method for large scale multi-class linear svms.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

A dual coordinate descent method for large-scale linear SVM.
Proceedings of the Machine Learning, 2008

2007
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization.
IEEE Trans. Neural Networks, 2007

Projected Gradient Methods for Nonnegative Matrix Factorization.
Neural Computation, 2007

A note on Platt's probabilistic outputs for support vector machines.
Mach. Learn., 2007

The NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 2007.
Proceedings of the TRECVID 2007 workshop participants notebook papers, 2007

Trust region Newton methods for large-scale logistic regression.
Proceedings of the Machine Learning, 2007

2006
A study on SMO-type decomposition methods for support vector machines.
IEEE Trans. Neural Networks, 2006

Generalized Bradley-Terry Models and Multi-Class Probability Estimates.
J. Mach. Learn. Res., 2006

Ranking individuals by group comparisons.
Proceedings of the Machine Learning, 2006

Combining SVMs with Various Feature Selection Strategies.
Proceedings of the Feature Extraction - Foundations and Applications, 2006

2005
Leave-One-Out Bounds for Support Vector Regression Model Selection.
Neural Computation, 2005

Working Set Selection Using Second Order Information for Training Support Vector Machines.
J. Mach. Learn. Res., 2005

Training Support Vector Machines via SMO-Type Decomposition Methods.
Proceedings of the Discovery Science, 8th International Conference, 2005

2004
Analysis of switching dynamics with competing support vector machines.
IEEE Trans. Neural Networks, 2004

Decomposition Methods for Linear Support Vector Machines.
Neural Computation, 2004

Probability Estimates for Multi-class Classification by Pairwise Coupling.
J. Mach. Learn. Res., 2004

A Generalized Bradley-Terry Model: From Group Competition to Individual Skill.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003
A study on reduced support vector machines.
IEEE Trans. Neural Networks, 2003

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel.
Neural Computation, 2003

Radius Margin Bounds for Support Vector Machines with the RBF Kernel.
Neural Computation, 2003

Special issue on support vector machines.
Neurocomputing, 2003

Decomposition methods for linear support vector machines.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

2002
A formal analysis of stopping criteria of decomposition methods for support vector machines.
IEEE Trans. Neural Networks, 2002

Errata to "A comparison of methods for multiclass support vector machines".
IEEE Trans. Neural Networks, 2002

Errata to "On the convergence of the decomposition method for support vector machines".
IEEE Trans. Neural Networks, 2002

Asymptotic convergence of an SMO algorithm without any assumptions.
IEEE Trans. Neural Networks, 2002

A comparison of methods for multiclass support vector machines.
IEEE Trans. Neural Networks, 2002

A Note on the Decomposition Methods for Support Vector Regression.
Neural Computation, 2002

Training <i>v</i> -Support Vector Regression: Theory and Algorithms.
Neural Computation, 2002

A Simple Decomposition Method for Support Vector Machines.
Mach. Learn., 2002

Analysis of Nonstationary Time Series Using Support Vector Machines.
Proceedings of the Pattern Recognition with Support Vector Machines, 2002

2001
On the convergence of the decomposition method for support vector machines.
IEEE Trans. Neural Networks, 2001

Formulations of Support Vector Machines: A Note from an Optimization Point of View.
Neural Computation, 2001

Training nu-Support Vector Classifiers: Theory and Algorithms.
Neural Computation, 2001

Optical Coating Designs Using the Family Competition Evolutionary Algorithm.
Evol. Comput., 2001

Solving General Capacity Problem by Relaxed Cutting Plane Approach.
Annals OR, 2001

2000
The analysis of decomposition methods for support vector machines.
IEEE Trans. Neural Networks Learn. Syst., 2000

1999
Incomplete Cholesky Factorizations with Limited Memory.
SIAM J. Sci. Comput., 1999

Newton's Method for Large Bound-Constrained Optimization Problems.
SIAM J. Optim., 1999

1998
Efficient test-point selection for scan-based BIST.
IEEE Trans. Very Large Scale Integr. Syst., 1998

An Unconstrained Convex Programming Approach to Linear Semi-Infinite Programming.
SIAM J. Optim., 1998

1997
A Hybrid Algorithm for Test Point Selection for Scan-Based BIST.
Proceedings of the 34st Conference on Design Automation, 1997

1995
Integration of partial scan and built-in self-test.
J. Electronic Testing, 1995

Timing-Driven Test Point Insertion for Full-Scan and Partial-Scan BIST.
Proceedings of the Proceedings IEEE International Test Conference 1995, 1995

1993
PSBIST: A Partial-Scan Based Built-In Self-Test Scheme.
Proceedings of the Proceedings IEEE International Test Conference 1993, Designing, Testing, and Diagnostics, 1993

Built-In Current Sensor for I<sub>DDQ</sub> Test in CMOS.
Proceedings of the Proceedings IEEE International Test Conference 1993, Designing, Testing, and Diagnostics, 1993

1991
Enhanced Controllability for <i>I<sub>DDQ</sub></i> Test Sets Using Partial Scan.
Proceedings of the 28th Design Automation Conference, 1991


  Loading...