text:   Regularization",7,"[""DL models tend to struggle with heteroskedastic and imbalanced datasets, where long-tailed labels have varying levels of uncertainty, partly bc it's hard to distinguish mislabeled, ambiguous, and rare examples. We propose a new regularization technique: <LINK> <LINK>"", 'The main principle is to regularize more strongly for those data that are rare and noisy. Joint work with @caokd8888, Yining Chen, @lu_junwei, Nikos Arechiga, @adnothing.', 'Conceptually, this can be viewed as a follow-up work of our last year NeurIPS paper on learning imbalanced datasets  https://t.co/IHbopWrwCD. In this paper, we need to deal with the interaction of the heteroskedasticity and imbalance more carefully.', '@huyhcmut1997 Thanks for the comments/questions. Yes, the idea can also be used for regression. Actually, the demonstrating examples in the intro are for the regression setting. To use it for regression, one can take equation (6) as the objective and choose $\\tau_i=\\simga_i^a/q_i^b$', '@huyhcmut1997 where a,b &gt; 0 are constants and \\sigma_i is the estimated standard deviation of the noise for that example (e.g., a=4/5, b=2/5 would the best choice predicted by the theory, but in fact, we think the exact choice does not matter that much as long as you tune lambda.)', '@huyhcmut1997 We will add a section on it in the next revision! Thanks!', '@chupvl @huyhcmut1997 Thanks for the comments. Hopefully my answer above to @huyhcmut also answers your question?']",20,06,1425