text: 65,90,1103858909041868800,18850305,Zachary Lipton,"New work by my student Yifan Wu identifies problems with traditional *deep domain adaptation* objectives & holes in the theory supporting it. Our paper offers new analysis, a new algorithm that escapes one identified failure mode, & experimental validation <LINK> Looking forward to discussing this work on Monday at Berkeley's ""Trustworthy Deep Learning"" seminar <LINK> @WilliamWangNLP Our key idea here is that enforcing strict alignment can often be a bad thing. For example, what if source and target distributions have different label distributions? Then alignment is actually lower-bounding your target error. Instead we optimize a relaxed objective.",https://arxiv.org/abs/1903.01689,"Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversarial approaches consist of aligning source and target encodings, often motivating this approach as minimizing two (of three) terms in a theoretical bound on target error. Unfortunately, this minimization can cause arbitrary increases in the third term, e.g. they can break down under shifting label distributions. We propose asymmetrically-relaxed distribution alignment, a new approach that overcomes some limitations of standard domain-adversarial algorithms. Moreover, we characterize precise assumptions under which our algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets. ",Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment,3,"['New work by my student Yifan Wu identifies problems with traditional *deep domain adaptation* objectives &amp; holes in the theory supporting it. Our paper offers new analysis, a new algorithm that escapes one identified failure mode, &amp; experimental validation <LINK>', 'Looking forward to discussing this work on Monday at Berkeley\'s ""Trustworthy Deep Learning"" seminar https://t.co/0il5wVTHef', '@WilliamWangNLP Our key idea here is that enforcing strict alignment can often be a bad thing. For example, what if source and target distributions have different label distributions? Then alignment is actually lower-bounding your target error. Instead we optimize a relaxed objective.']",19,03,656