{"id":6643,"date":"2022-10-06T14:18:51","date_gmt":"2022-10-06T18:18:51","guid":{"rendered":"https:\/\/carleton.ca\/cuids\/?post_type=cu-events&p=6643"},"modified":"2023-03-15T11:12:39","modified_gmt":"2023-03-15T15:12:39","slug":"using-data-from-various-sources-to-facilitate-learning-and-inference","status":"publish","type":"cu-events","link":"https:\/\/carleton.ca\/cuids\/cu-events\/using-data-from-various-sources-to-facilitate-learning-and-inference\/","title":{"rendered":"Recorded – Using Data from Various Sources to Facilitate Learning and Inference"},"content":{"rendered":"

CUIDS Distinguished Speaker Series<\/span><\/strong><\/p>\n

Using Data from Various Sources to Facilitate Learning and Inference<\/strong>
\nDr. Junfeng Wen<\/p>\n

Nowadays, we have access to millions of data examples from various sources. However, not all of them are relevant to the problem we are trying to solve at hand. In order to train an effective machine learning model, one needs to select the most useful data to conduct learning and inference, which is Junfeng’s research focus. In this talk, Junfeng will first provide an overview of his research in transfer learning, reinforcement learning and federated learning. Then he will showcase a few developed algorithms, including multi-source domain adaptation and personalized federated learning.<\/p>\n

Junfeng Wen is an Assistant Professor in the School of Computer Science at Carleton University. He obtained his Ph.D. in Statistical Machine Learning from the Department of Computing Science at the University of Alberta. His research focuses on how to leverage data collected from various sources to build effective models for a different but related target domain in the fields of machine learning (ML) and reinforcement learning (RL).<\/p>\n\n\n\n
Seminar Moderator:<\/strong><\/p>\n

Dr. Alan Tsang<\/a> <\/b>Assistant Professor, School of Computer Science, Carleton University<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\t

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