Learning from Imbalanced Data: Progress and Challenges

Speaker:  Nitesh Chawla – Notre Dame, IN, United States
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Abstract

Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. With modern advances and rapid developments in deep learning, countering the problem of imbalanced data has become extremely important. In this talk, I will share the progress on learning from imbalanced datasets, contrast with the “generative” synthetic data generation approaches, and reflect on open challenges and application domains. I will also discuss the similarities between fair ML and imbalanced data, and where they complement each other. I will conclude the talk with use-cases and applications. 

About this Lecture

Number of Slides:  45
Duration:  60 minutes
Languages Available:  English
Last Updated: 

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