Chih-Jen Lin's recent talks (full list)

• Multi-label classification: status and challenges . Talk at Shanghai Jiao Tong University and Fudan University, November 14-15, 2023
• On the ``rough use'' of machine learning techniques. Keynote at SIGIR, July 25, 2023
• Development of open-source machine learning packages . Talk at MBZUAI, April 4, 2023
• Algorithms and software for text classification. Virtual talk at Bloomberg, November 14, 2022
• Optimization and machine learning. Plenary talk at TWSIAM annual meeting, July 24, 2020
• Newton method for convolutional neural networks. Talk at UCLA Statistics department, November 12, 2019
• Lessons learned from developing machine learning algorithms and systems. Kenote at Technologies and Applications of Artificial Intelligence (TAAI 2017), December 2017. An earlier version was given as an invited talk at Nanyang Technological University, Singapore, January 2017
• Large-scale Linear Classification: Status and Challenges . Invited talk at Criteo Machine Learning workshop, Paris, November, 2017
• Optimization and machine learning. Invited talk at Summer School on Optimization, Big Data and Applications, Italy, July, 2017
• Matrix factorization and factorization machines for recommender systems . Keynote at at 4th Workshop on Large-Scale Recommender Systems, ACM RecSys, September 2016
• When and when not to use distributed machine learning . Keynote at International Winter School on Big Data, Bilbao, Spain, February 2016
• Large-scale linear classification. Course at International Winter School on Big Data, February 2016
• Matrix factorization and factorization machines for recommender systems. Talk at Facebook, November 13, 2015.
• Large-scale Linear and Kernel Classification. Invited talk at Microsoft Research India Machine Learning Summer School, June 15, 2015.
• Matrix factorization and factorization machines for recommender systems. Invited talk at SDM workshop on Machine Learning Methods on Recommender Systems, May 2, 2015.
• Large-scale Linear Classification: Status and Challenges. Talk at San Francisco Machine Learning Meetup, October 30, 2014 (video)
• Big-data analytics: challenges and opportunities. Keynote speech at Taiwan Data Science Conference, Taipei, August 30, 2014.
• Large-scale Linear Classification. Talk at Criteo, August 1, 2014.
• Distributed data classification. Invited talk at Workshop on New Learning Frameworks and Models for Big Data, ICML, June 25, 2014. (also invited talk at Workshop on Scalable Data Analytics, PAKDD, May 13, 2014.)
• Recent advances in large linear classification. Invited talk at Asian Conference on Machine Learning (ACML), November 14, 2013
• Experiences and Lessons in Developing Machine Learning and Data Mining Software . Invited talk at Chinese R Conference, November 2, 2013.
• Optimization Methods for Large-scale Linear Classification . Talk at University of Rome "La Sapienza," June 24, 2013.
• Optimization and Machine Learning . 25th Simon Stevin Lecture, K. U. Leuven Optimization in Engineering Center, January 17, 2013
• Large-scale machine learning in distributed environments . Tutorial talk at K. U. Leuven Optimization in Engineering Center, January 16, 2013.
• Support vector machines and kernel methods: status and challenges . Tutorial talk at K. U. Leuven Optimization in Engineering Center, January 15, 2013.
• Machine learning software: design and practical use. Invited talk at Machine learning summer school (MLSS), Kyoto, 2012. A shorter version was given at another MLSS, Santa Cruz, 2012.
• Experiences and lessons in developing industry-strength machine learning and data mining software. Invited talk at Industry Practice Expo of ACM KDD 2012, Beijing, August 2012.
• Large-scale machine learning in distributed environments . Tutorial talk at ICMR 2012, Hong Kong, June 5, 2012
• Recent advances in large linear classification. Talk at NEC Labs. Cupertino, August 27, 2011.
• Support Vector Machines and Kernel Methods. Plenary talk at International Workshop on Recent Trends in Learning, Computation, and Finance, Pohang, Korea, August 30, 2010.
• Feature Engineering and Classifier Ensemble for KDD Cup 2010 Talk at KDD cup workshop, July 25, 2010. This talk explains our approach for winning KDD cup 2010. A more complete version is here
• Training support vector machines: status and challenges Talk at Microsoft Research Asia, October 13, 2009
• Training large-scale linear classifiers Talk at Hong Kong Univ. of Science and Technology, February 5, 2009
• Training support vector machines: status and challenges Invited talk at ICML 2008 Workshop on Large Scale Learning Challenge.
• Support vector machines: status and challenges Talk at Caltech, November 14, 2006
• Support vector machines Tutorial talk at Machine learning summer school, Taipei, 2006. Slides of this talk may be outdated. Please check a more recent talk instead.
• Ranking Individuals by Group Comparisons Talk at International Conference on Machine Learning, June 2006.
• Working Set Selection Using Second Order Information for Training SVM Talk at Workshop on Large Scale Kernel Machines, NIPS 2005.
• Optimization, Support Vector Machines, and Machine Learning. Talk in DIS, University of Rome and IASI, CNR, Italy. September 1-2, 2005. This is a short course introducing optimization researchers about SVM research.
• Support vector machines for data classification. Talk in CWI (Dutch National Research Institute for Mathematics and Computer Science). February 9, 2004.
• Some thoughts on machine learning software design. Talk in University of Southampton, Februaryd 6, 2004.
• A practical guide to support vector classification Talk in University of Freiburg, July 15, 2003.
• Can support vector machines become a major classification method ? Talk in Max Planck Institute, January 29, 2003. PDF file
• Support Vector Machines for Data Classification and Regression Talk in Merck Research Lab., August 16, 2002.
• EUNITE Competition: Electricity Load Forecasting Talk in EUNITE 2001 (winner of EUNITE competition), December 14, 2001.
• Support Vector Machines for Data Classification and Regression Talk in Ford Scientific Research Lab., July 24, 2001. (a similar talk was given at Agilent Inc., July 31, 2001)
• IJCNN 2001 Challenge: Generalization Ability and Text Decoding Talk in IJCNN (winner of IJCNN Challenge), July 17 2001.
• Implementation of support vector machines software: theory and practice Talk in Department of Electrical Engineering, Ohio State University, August 29, 2000.
• The analysis of decomposition methods for support vector machines. Talk in IJCAI 99, Support vector machine workshop. Stockholm, Aug 2, 1999.
• Solving Structural Optimization Problems via Semidefinite Programming. Talk in INFORMS annual meeting, Seattle, October 25-28, 1998.
• Preconditioning dense linear systems from large-scale semidefinite programming problems. Talk in Fifth Copper Mountain Conference on Iterative Methods, Copper Mountain, Colorado, April, 1998.
• Incomplete Cholesky factorizations with limited memory. Talk in Fourth Kalamazoo Symposium on Matrix Analysis & Applications, Kalamazoo, MI, Oct. 24-25, 1997
• Newton's method for large bound-constrained optimization problems. Talk in International Symposium of Mathematical Programming, Lausanne, Switzerland, August 24-29, 1997.