Hsuan-Tien Lin > Presentations

Hsuan-Tien Lin

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Talks

Attempts Towards Controllable Generation . Annual Statistics Conference at NTHU STAT, Hsinchu, Taiwan, December 2023.
Machine Learning for Modern Artificial Intelligence. NCHU AM Seminar, Taichung, Taiwan, November 2023.
Machine Learning for Modern Artificial Intelligence. CGU AI Program Seminar, Taoyuan (virtual), Taiwan, November 2023.
Machine Learning for Modern Artificial Intelligence. NCKU Physics Seminar, Tainan (virtual), Taiwan, September 2023.
Attempts Towards Controllable Generation . NTUILO x MITILP Consortia, Taipei (virtual), Taiwan, August 2023.
開發機器的學習潛能 (Developing the Learning Potential of Machines, in Traditional Chinese). Taipei District Court, Taipei, Taiwan, May 2023.
Machine Learning for Modern Artificial Intelligence. Fubon Securities, Taipei, Taiwan, April 2023.
Machine Learning for Modern Artificial Intelligence. Keynote talk in Taiwanese American Industrial Technology Association-Silicon Valley Annual Conference, Fremont, CA, USA, September 2022.
Learning for Big Data. Taiwan Association on Cancer Research, New Taipei, Taiwan, July 2022.
Active Learning by Learning. NTHU ISA Seminar, Hsinchu (virtual), Taiwan, March 2022.
Learning with Limited Labeled Data. Artificial Intelligence and Data Science Workshop, Hualien, Taiwan, January 2022.
Machine Learning for Modern Artificial Intelligence. Artificial Intelligence and Data Science Workshop, Hualien, Taiwan, January 2022.
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. South Taiwan Statistics Conference, Kaohsiung, Taiwan, October 2021.
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. AI Forum, Taipei (virtual), Taiwan, October 2021.
Cost-sensitive Classification: Techniques and Stories. MLSS 2021, Taipei, Taiwan, August 2021.
Machine Learning for Modern Artificial Intelligence. Winbond Corporation, Internal Webinar, July 2021.
Active Learning by Learning. IEEE Computational Intelligence Society, International Webinar, April 2021. (Video)
Active Learning by Learning. UBC Centre for Artificial Intelligence Decision-making and Action, International Webinar, January 2021. (Video)
Machine Learning for Modern Artificial Intelligence. International Computer Symposium, Tainan, Taiwan, December 2020.
MUSE Teaching. MOE AI Course Program, Hsinchu, Taiwan, September 2020.
Machine Learning for Modern Artificial Intelligence. Wistron NeWeb Corporation, Hsinchu, Taiwan, December 2019.
Making Active Learning Realistic. Keynote talk in Weakly-supervised Learning Workshop @ ACML 2019, Nagoya, Japan, November 2019.
Machine Learning Overviews and Applications. NTU BIME Seminar, Taipei, Taiwan, October 2019.
Label Space Coding for Multi-label Classification. RIKEN Center for Advanced Intelligence Project, Tokyo, Japan, August 2019.
Advances in Cost-sensitive Multiclass and Multilabel Classification. KDD 2019 Tutorial, Anchorage, Alaska, USA, August 2019.
Machine Learning for Artificial Intelligence in Medicine Applications. Information Technology Office, National Taiwan University Hospital, Taipei, Taiwan, July 2019.
Machine Learning for Modern Artificial Intelligence. Speech Signal Processing Workshop, Taipei, Taiwan, June 2019.
Mathematical Machine Learning for Modern Artificial Intelligence. TWSIAM, Hsinchu, Taiwan, May 2019.
Machine Learning for Artificial Intelligence in Medicine Applications. Division of Hematology and Oncology, Chang Gung Memorial Hospital, New Taipei, Taiwan, March 2019.
Machine Learning for Modern Artificial Intelligence. Institute of Information Science, Academia Sinica, Taipei, Taiwan, November 2018.
開發機器的學習潛能 (Developing the Learning Potential of Machines, in Traditional Chinese). 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.
Label Space Coding for Multi-label Classification. TWSIAM, Taipei, Taiwan, May 2018.
Active Learning by Bandit Learning. National Center for Theoretical Sciences @ NTU, Taipei, Taiwan, March 2018.
From Big Data to Artificial Intelligence. National Taiwan University EiMBA Program Guest Lecture, Taipei, Taiwan, March 2018
From Cloud to Artificial Intelligence. CIO Value Institute, Taipei, Taiwan, March 2018
Label Space Coding for Multi-label Classification. Institute of Statistics, National University of Kaohsiung, Kaohsiung, Taiwan, December 2017.
Cost-sensitive Classification: Algorithm and Application. NSYSU AI in Data Science Forum, Kaohsiung, Taiwan, December 2017.
Choices for Smarter AI. Keynote talk in PyCon Taiwan, Taipei, Taiwan, June 2017.
Active Learning by Bandit Learning. A Symposium on Complex Data Analysis, Hsinchu, Taiwan, May 2017.
Quick Tour of Machine Learning. Lecture Series of Data Science Enthusiasts in Taiwan, Taipei, Taiwan, March 2017.
Machine Learning Overviews and Applications. Industrial Technology Research Institute, Hsinchu, Taiwan, January 2016.
Active Learning by Learning. IR Workshop, Taipei, Taiwan, December 2015.
Quick Tour of Machine Learning. Lecture Series of Data Science Enthusiasts in Taiwan, Taipei, Taiwan, December 2015.
Machine Learning Overviews and Applications. CJCU IM Seminar, Tainan, Taiwan, November 2015.
Label Space Coding for Multi-label Classification. Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, September 2015.
My RIGHT-O Lab. NTU New Faculty Orientation, Hsitou, Taiwan, September 2015.
Learning for Big Data. Annual Meeting of Data Science Enthusiasts in Taiwan, Taipei, Taiwan, August 2015.
Competition-Based Final Projects for Enhancing Students' Motivations (in Traditional Chinese). NTU Teaching Series, Taipei, Taiwan, June 2015.
Active Learning by Learning. AI Forum, Kaohsiung, Taiwan, June 2015.
Label Space Coding for Multi-label Classification. TWSIAM, Kaohsiung, Taiwan, May 2015.
Learning for Big Data. Keynote talk in IEEE BigData 2015 Taipei Satellite Session, Taipei, Taiwan, May 2015.
Machine Learning Overviews and Applications. Mediatek, Hsinchu, Taiwan, April 2015.
Label Space Coding for Multi-label Classification. Intel Web Seminar, Taipei, Taiwan, February 2015.
Contract Bridge Bidding by Learning. Workshop on Computer Poker and Imperfect Information @ AAAI 2015, Texas, Austin, USA, January 2015.
Cost-sensitive Multiclass Classification Using One-versus-one Comparisons. NTHU STAT Seminar, Hsinchu, Taiwan, December 2014.
Pseudo-Reward Algorithms for Contextual Bandits with Linear Payoff Functions. ACML 2014, Nha Trang, Vietnam, November 2014.
Reduction from cost-sensitive multiclass classification to one-versus-one binary classification. ACML 2014, Nha Trang, Vietnam, November 2014.
Basics of Machine Learning. Big Data and Machine Learning Summer School, Hsinchu, Taiwan, July 2014.
Machine Learning Overviews and Applications. Marvell Taiwan, Hsinchu, Taiwan, May 2014.
Machine Learning Approaches for Interactive Verification. PAKDD 2014, Tainan, Taiwan, May 2014.
Label Space Coding for Multi-label Classification. NTHU Comm Seminar, Hsinchu, Taiwan, April 2014.
Basics of Machine Learning. Chien-Kuo Senior High School Talented Class, Taipei, Taiwan, November 2013.
Active Ranking of Pairs and Points for Large-scale Linear Bipartite Ranking. ACML 2013, Canberra, Australia, November 2013.
Cost-sensitive Classification: Algorithms and Advances. ACML 2013 Tutorial, Canberra, Australia, November 2013.
Cost-sensitive Multiclass Classification via Regression. NTU CE Seminar, Taipei, Taiwan, October 2013.
Cost-sensitive Multiclass Classification via Regression. NCU CSIE Seminar, Taoyuan, Taiwan, October 2013.
Feature-aware Label Space Dimension Reduction for Multi-label Classification. AI Forum, Taipei, Taiwan, May 2013.
Label Space Coding for Multi-label Classification. NCTU CS Seminar, Hsinchu, Taiwan, May 2013.
Deepening Learning Starts after Class (in Traditional Chinese). NTU Teaching Series, Taipei, Taiwan, March 2013.
Feature-aware Label Space Dimension Reduction for Multi-label Classification. Taiwan-Israel Joint Workshop on Artificial Intelligence, Tainan, Taiwan, November 2012.
Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression. Preference Learning Stream @ EURO 2012, Vilnius, Lithuania, July 2012.
Teaching Machine Learning to a Diverse Audience: the Foundation-based Approach. Teaching Machine Learning Workshop @ ICML 2012, Edinburgh, United Kindom, June 2012.
Cost-sensitive Multiclass Classification Using One-versus-one Comparisons. ICISE 2012, Tainan, Taiwan, June 2012.
Label Space Coding for Multi-label Classification. IIS Sinica Seminar, Taipei, Taiwan, April 2012.
Multi-label Active Learning with Auxiliary Learner. ACML 2011, Taoyuan, Taiwan, November 2011. (Paper).
Cost-sensitive Multiclass Classification via Regression. NCCU CS Seminar, Taipei, Taiwan, November 2011.
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. CITI Sinica Seminar, Taipei, Taiwan, October 2011.
A Linear Ensemble of Individual and Blended Models for Music Rating Prediction. KDDCup 2011, San Diego, CA, USA, August 2011. (Paper)
Cost-sensitive Multiclass Classification via Regression. NTNU CSIE Seminar, Taipei, Taiwan, December 2010.
Cost-sensitive Classification: Status and Beyond. Machine Learning Research in Taiwan@TAAI 2010, Hsinchu, Taiwan, November 2010. (Paper).
An Ensemble Solution for Learning to Rank. Yahoo! Learning to Rank Challenge@ICML 2010, Haifa, Israel, June 2010. (Paper)
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification. ICML 2010, Haifa, Israel, June 2010. (Paper).
Cost-sensitive Multiclass Classification via Regression. CITI Sinica Seminar, Taipei, Taiwan, May 2010.
Cost-sensitive Multiclass Classification Using One-versus-one Comparisons. NTHU EE Seminar, Hsinchu, Taiwan, April 2010.
From Ordinal Ranking to Binary Classification. NDHU AM Seminar, Hualian, Taiwan, February 2010.
Combining Ordinal Preferences by Boosting. Preference Learning Workshop @ ECML/PKDD 2009, Bled, Slovenia, September 2009. (Paper).
From Ordinal Ranking to Binary Classification. NTUST CS Seminar, Taipei, Taiwan, March 2009.
From Ordinal Ranking to Binary Classification. Microsoft Research Asia, Beijing, China, February 2009.
A Simple Algorithm for Cost-Sensitive Classification. NTU CSIE Seminar, Taipei, Taiwan, September 2008.
From Ordinal Ranking to Binary Classification. NTHU CS Seminar, Hsinchu, Taiwan, March 2008.
From Ordinal Ranking to Binary Classification. NCTU CS Seminar, Hsinchu, Taiwan, March 2008.
From Ordinal Ranking to Binary Classification. NTU CSIE Seminar, Taipei, Taiwan, March 2008.
From Ordinal Ranking to Binary Classification. Caltech CS/IST Lunch Bunch, Pasadena, CA, U.S.A., March 2008.
Perceptron Learning by Random Coordinate Descent. IJCNN '07, Orlando, FL, U.S.A., August 2007. (Paper).
Automatic Ranking by Extended Binary Classification. Institute of Information Science, Academia Sinica, Taipei, Taiwan, March 2007.
Ordinal Regression by Extended Binary Classification. NIPS '06 Spotlight, Vancouver, Canada, December 2006. (Poster), (Paper).
Automatic Ranking by Extended Binary Classification. Caltech EE Fall '06 Seminar, Pasadena, CA, U.S.A., November 2006.
Large-Margin Thresholded Ensembles for Ordinal Regression. ALT '06, Barcelona, Spain, October 2006. (Paper).
Large-Margin Thresholded Ensembles for Ordinal Regression. MLSS '06 Workshop, Taipei, Taiwan, July 2006.
Distance Based SVM Kernels for Infinite Ensemble Learning. ICONIP '05, Taipei, Taiwan, November 2005. (Paper).
Analysis of SAGE Results with Combined Learning Techniques. ECML/PKDD Discovery Challenge Workshop '05, Porto, Portugal, October 2005. (Paper).
Infinite Ensemble Learning with Support Vector Machinery. ECML '05, Porto, Portugal, October 2005. (Paper).
Infinite Ensemble Learning with Support Vector Machines. Caltech Second Symposium on Vision and Learning, Pasadena, CA, U.S.A., September 2005.

Posters

Feature-aware Label Space Dimension Reduction for Multi-label Classification. NIPS '12, Lake Tahoe, NV, U.S. A., December 2012.
Ordinal Regression by Extended Binary Classification. NIPS '06, Vancouver, Canada, December 2006. (Spotlight), (Paper).

Lectures

Brief Introduction to LEMGA. Minitalk for Caltech CS 156b (Winter '06): Learning Systems, Pasadena, CA, U.S.A., January 2006. (Software).
Introduction to Support Vector Machines. Invited talk for NTU Speech Processing Lab, Taipei, Taiwan, November 2005.
Introduction to Boosting and Joint Boosting. Guest lecture for Caltech EE148 (Spring '05): Machine Learning for Computer Vision, Pasadena, CA, U.S.A., April 2005.

Last updated at CST 00:06, December 05, 2023
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