{"id":688176,"date":"2020-08-28T13:57:30","date_gmt":"2020-08-28T20:57:30","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=688176"},"modified":"2020-08-28T13:57:30","modified_gmt":"2020-08-28T20:57:30","slug":"importance-weighted-active-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/importance-weighted-active-learning\/","title":{"rendered":"Importance Weighted Active Learning"},"content":{"rendered":"<blockquote class=\"abstract mathjax\"><p>We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. Experiments on passively labeled data show that this approach reduces the label complexity required to achieve good predictive performance on many learning problems.<\/p><\/blockquote>\n<div class=\"metatable\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. Experiments on passively labeled data show that this approach reduces the 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