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#leveraging

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JMLR<p>&#39;DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data&#39;, by Jiayi Tong, Jie Hu, George Hripcsak, Yang Ning, Yong Chen.</p><p><a href="http://jmlr.org/papers/v26/23-1254.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v26/23-1254.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/leveraging" class="mention hashtag" rel="tag">#<span>leveraging</span></a> <a href="https://sigmoid.social/tags/estimates" class="mention hashtag" rel="tag">#<span>estimates</span></a> <a href="https://sigmoid.social/tags/covariate" class="mention hashtag" rel="tag">#<span>covariate</span></a></p>
Susan Larson ♀️🏳️‍🌈🏳️‍⚧️🌈<p>“The <a href="https://mastodon.online/tags/CassReview" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CassReview</span></a>’s departure from the <a href="https://mastodon.online/tags/evidentiary" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>evidentiary</span></a> and <a href="https://mastodon.online/tags/procedural" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>procedural</span></a> <a href="https://mastodon.online/tags/standards" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>standards</span></a> of <a href="https://mastodon.online/tags/medical" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>medical</span></a> <a href="https://mastodon.online/tags/law" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>law</span></a>, <a href="https://mastodon.online/tags/policy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>policy</span></a> and <a href="https://mastodon.online/tags/practice" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>practice</span></a> can be understood best in the context of the <a href="https://mastodon.online/tags/history" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>history</span></a> of <a href="https://mastodon.online/tags/leveraging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>leveraging</span></a> <a href="https://mastodon.online/tags/medicine" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>medicine</span></a> to <a href="https://mastodon.online/tags/police" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>police</span></a> <a href="https://mastodon.online/tags/gendernorms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gendernorms</span></a>.</p>
JMLR<p>&#39;Adaptation Augmented Model-based Policy Optimization&#39;, by Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang.</p><p><a href="http://jmlr.org/papers/v24/22-0606.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/22-0606.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/adaptation" class="mention hashtag" rel="tag">#<span>adaptation</span></a> <a href="https://sigmoid.social/tags/leveraging" class="mention hashtag" rel="tag">#<span>leveraging</span></a> <a href="https://sigmoid.social/tags/learned" class="mention hashtag" rel="tag">#<span>learned</span></a></p>
Published papers at TMLR<p>A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods</p><p>Tiago Salvador, Kilian FATRAS, Ioannis Mitliagkas, Adam M Oberman</p><p>Action editor: Mingsheng Long.</p><p><a href="https://openreview.net/forum?id=XcVzIBXeRn" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=XcVzIB</span><span class="invisible">XeRn</span></a></p><p><a href="https://sigmoid.social/tags/adaptation" class="mention hashtag" rel="tag">#<span>adaptation</span></a> <a href="https://sigmoid.social/tags/classifying" class="mention hashtag" rel="tag">#<span>classifying</span></a> <a href="https://sigmoid.social/tags/leveraging" class="mention hashtag" rel="tag">#<span>leveraging</span></a></p>
Published papers at TMLR<p>MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information</p><p>Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer</p><p><a href="https://openreview.net/forum?id=5aYGXxByI6" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=5aYGXx</span><span class="invisible">ByI6</span></a></p><p><a href="https://sigmoid.social/tags/ai" class="mention hashtag" rel="tag">#<span>ai</span></a> <a href="https://sigmoid.social/tags/learned" class="mention hashtag" rel="tag">#<span>learned</span></a> <a href="https://sigmoid.social/tags/leveraging" class="mention hashtag" rel="tag">#<span>leveraging</span></a></p>
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