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| Name | Learning from MASSIVE data feat UCLA Prof Baharan Mirzasoleiman | Stanford MLSys #45 |
| Video Uploader | Video From Stanford MLSys Seminars |
| Upload Date | This Video Uploaded At 04-11-2021 21:38:26 |
| Video Discription | Episode 45 of the Stanford MLSys Seminar Series! Data-efficient and Robust Learning from Massive Datasets Speaker: Baharan Mirzasoleiman Abstract: Large datasets have been crucial to the success of modern machine learning models. However, training on massive data has two major limitations. First, it is contingent on exceptionally large and expensive computational resources, and incurs a substantial cost due to the significant energy consumption. Second, in many real-world applications such as medical diagnosis, self-driving cars, and fraud detection, big data contains highly imbalanced classes and noisy labels. In such cases, training on the entire data does not result in a high-quality model. In this talk, I will argue that we can address the above limitations by developing techniques that can identify and extract the representative subsets for learning from massive datasets. Training on representative subsets not only reduces the substantial costs of learning from big data, but also improves their accuracy and robustness against noisy labels. I will discuss how we can develop theoretically rigorous techniques that provide strong guarantees for the quality of the extracted subsets, as well as the learned models’ quality and robustness against noisy labels. I will also show the effectiveness of such methods in practice for data-efficient and robust learning. Bio: Baharan Mirzasoleiman is an Assistant Professor in the Computer Science Department at UCLA. Her research focuses on developing new methods that enable efficient machine learning from massive datasets. Her methods have immediate application to high-impact problems where massive data volumes prohibit efficient learning and inference, such as huge image collections, recommender systems, Web and social services, video and other large data streams. Before joining UCLA, she was a postdoctoral research fellow in Computer Science at Stanford University. She received her Ph.D. in Computer Science from ETH Zurich. She received an ETH medal for Outstanding Doctoral Thesis, and was selected as a Rising Star in EECS by MIT. -- Stanford MLSys Seminar hosts: Dan Fu, Karan Goel, Fiodar Kazhamiaka, and Piero Molino Executive Producers: Matei Zaharia, Chris Ré Twitter: https://twitter.com/realDanFu https://twitter.com/krandiash https://twitter.com/w4nderlus7 Intro music: Heading Home by Nekzlo @nekzlo Music provided by Free Music for Vlogs youtu.be/YGL35p7LLqo -- 0:00 Starting soon 0:12 Presentation 33:53 Discussion Check out our website for the schedule: http://mlsys.stanford.edu Join our mailing list to get weekly updates: https://groups.google.com/forum/#!forum/stanford-mlsys-seminars/join #machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford #ucla #massivedatasets Bmn8SNojTlA |
| Category | Education |
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