Computer Scientists Want To Make Robots Forget Their Bad Data

When “bad” data gets sucked into a machine learning system — that’s how Alan Greenspan put it when discussing the computer models that failed to predict the 2008 recession — that information can be hard to dislodge. But a new concept, proposed by computer scientists Junfeng Yang and Yinzhi Cao, of Columbia University and Lehigh University, respectively, brings the idea of unlearning to computers. As Cao and Yang write in the abstract published for the 2015 IEEE Xplore conference, you don’t have to go all the way back to square one to forget: To forget a training data sample, our approach simply updates a small number of summations — asymptotically faster than retraining from scratch. Our approach is general, because the summation form is from the statistical query learning in…


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