MLSS 2021
Machine Learning Summer School 2021
Machine Learning Summer School 2021
The machine learning summer school (MLSS) series was started in 2002 with the motivation to promulgate modern methods of statistical machine learning and inference. It was motivated by the observation that many students are keen to learn about machine learning, and an increasing number of researchers want to apply machine learning methods to their research problems. Machine learning summer schools present topics that are at the core of modern Machine Learning, from fundamentals to state-of-the-art practice. The speakers are leading experts in their fields who talk with enthusiasm about their subjects.
MLSS 2021 TAIPEI https://ai.ntu.edu.tw/mlss2021/
▍Speaker: Csaba Szepesvari
Title: Large scale learning and planning in reinforcement learning
▍Speaker: Hsuan-Tien Lin
Title: Cost-sensitive Classification: Techniques and Stories
▍Speaker: Jason Lee
Title: Theory of deep learning
▍Industrial Talk: Appier Inc.
▍Speaker: Shou-De Lin
Title: Machine Learning as a Services: Challenges and Opportunities
▍Speaker: Henry Kautz
Title: Neuro-symbolic systems and the history of AI
▍Speaker: Chun-Yi Lee
Title: Fundamentals and Applications of Deep Reinforcement Learning
▍Industrial Talk: Perfect Corp.
▍Speaker: Johnny Tseng
Title: Transform the Beauty Industry through AI + AR: Perfect Corp’s Innovative Vision into the Digital Era
▍Speaker: Marco Cuturi
▍Title: Optimal transport
▍Speaker: Kai-Wei Chang
Title: Bias and Fairness in NLP
▍Industrial Talk: CyberLink Corp.
▍Speaker: David Lee
Title: Developing a World-Class AI Facial Recognition Solution – CyberLink FaceMe®
▍Speaker: Cho-Jui Hsieh
Title: Neural Architecture Search and AutoML
▍Speaker: Pin-Yu Chen
Title: Holistic Adversarial Robustness for Deep Learning
▍Speaker: John Shawe-Taylor
Title: An introduction to Statistical Learning Theory and PAC-Bayes Analysis
▍Speaker: Hung-Yi Lee
Title: Deep Learning for Speech Processing
▍Speaker: Philipp Krähenbühl
Title: Computer Vision
▍Speaker: Song Han
Title: TinyML and Efficient Deep Learning
▍Speakers: Thang Vu, Shang-Wen Li
Title: Meta Learning for Human Language Processing
▍Speaker: Been Kim
Title: Interpretable machine learning
▍Speaker: Karteek Alahari
Title: Continual Visual Learning
▍Speaker: Michael Bronstein
Title: Geometric Deep Learning
▍Speaker: Shou-De Lin
Title: Machine Learning in Practice – what to do if my ML models fail to achieve a desirable quality ?
▍Panel Discussion
Panelists:
* Cho-Jui Hsieh
* Pin-Yu Chen
* Soheil Feizi
* Sijia Liu
Title: Trustworthy Machine Learning: Challenges and Opportunities
▍Panel Discussion
Panelists:
* Shinji Watanabe
* Shang-Wen Li
* Mirco Ravanelli
* Titouan Parcollet
Title:Self-supervised Learning and Universal Modeling for Speech and Audio Processing – Benchmarking and Open-source Toolkits
▍Speaker: Arthur Gretton
Title: Probability Divergences and Generative Models