Adapting to Distribution Shifts: Recent Advances in Importance Weighting Methods
Monday, April 24, 2023
Distribution shifts are conceivable in practical machine learning scenarios, such as when a model is trained on data collected in different environments, or when a model is used in a test environment that is different from the training environment. The use of an importance-weighted loss function is a classical approach to deal with such distribution shifts. In this talk, I will give an overview of our recent advances in importance-based distribution shift adaptation methods, including joint importance-predictor estimation for covariate shift adaptation, dynamic importance weighting for joint distribution shift adaptation, and multistep class prior shift adaptation.
Post Talk Link: Click Here
Passcode: rSJC3U+A
Speaker/s
Masashi Sugiyama received his Ph.D. in Computer Science from the Tokyo Institute of Technology in 2001. He has been a professor at the University of Tokyo since 2014, and simultaneously the director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016. His research interests include theories and algorithms of machine learning. In 2022, he received the Award for Science and Technology from the Japanese Minister of Education, Culture, Sports, Science and Technology. He was program co-chair of the Neural Information Processing Systems (NeurIPS) conference in 2015, the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2019, and the Asian Conference on Machine Learning (ACML) in 2010 and 2020. He is (co-)author of Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman & Hall, 2015), Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015), and Machine Learning from Weak Supervision (MIT Press, 2022).
Related
Polygenic Score Modeling to Investigate Genotype-Phenotype Associations
Read More
Trustworthy Machine Learning: Transparency, Collaboration, and Evaluation
Read More