{"id":45643,"date":"2023-10-09T14:18:54","date_gmt":"2023-10-09T06:18:54","guid":{"rendered":"https:\/\/swarma.org\/?p=45643"},"modified":"2023-10-09T14:18:54","modified_gmt":"2023-10-09T06:18:54","slug":"nat-mach-intell-%e9%80%9f%e9%80%92%ef%bc%9a%e5%9b%a0%e6%9e%9c%e6%a8%a1%e5%9e%8b%e4%b8%ad%e6%9c%80%e4%bc%98%e5%b9%b2%e9%a2%84%e8%ae%be%e8%ae%a1%e7%9a%84%e4%b8%bb%e5%8a%a8%e5%ad%a6%e4%b9%a0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=45643","title":{"rendered":"Nat. Mach. Intell. \u901f\u9012\uff1a\u56e0\u679c\u6a21\u578b\u4e2d\u6700\u4f18\u5e72\u9884\u8bbe\u8ba1\u7684\u4e3b\u52a8\u5b66\u4e60"},"content":{"rendered":"
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\u5173\u952e\u8bcd\uff1a\u56e0\u679c\u79d1\u5b66\uff0c\u56e0\u679c\u4e3b\u52a8\u5b66\u4e60\uff0c\u5e72\u9884\u8bbe\u8ba1<\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/p>\n<\/section>\n

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\u8bba\u6587\u6807\u9898\uff1aActive learning for optimal intervention design in causal models<\/span><\/section>\n
\u8bba\u6587\u6765\u6e90\uff1aNature Machine Intelligence<\/em><\/span><\/section>\n
\u6591\u56fe\u94fe\u63a5\uff1ahttps:\/\/pattern.swarma.org\/paper\/5ece4d52-6178-11ee-b6f3-0242ac17000d<\/span><\/section>\n
\u539f\u6587\u94fe\u63a5\uff1ahttps:\/\/www.nature.com\/articles\/s42256-023-00719-0<\/span><\/section>\n
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\u5728\u5404\u4e2a\u9886\u57df\uff0c\u5305\u62ec\u79d1\u5b66\u3001\u5de5\u7a0b\u548c\u516c\u5171\u653f\u7b56\uff0c\u90fd\u5b58\u5728\u4e00\u9879\u5173\u952e\u95ee\u9898\uff0c\u5373\u5982\u4f55\u8fdb\u884c\u987a\u5e8f\u5b9e\u9a8c\u8bbe\u8ba1<\/span>\uff08Sequential experimental design\uff09<\/span>\uff0c\u4ee5\u53d1\u73b0\u5b9e\u73b0\u6240\u671f\u671b\u7ed3\u679c\u7684\u5e72\u9884\u63aa\u65bd\u3002\u5f53\u53ef\u80fd\u7684\u5e72\u9884\u7a7a\u95f4\u5f88\u5927\uff0c\u4f7f\u8be6\u5c3d\u65e0\u9057\u7684\u641c\u7d22\u53d8\u5f97\u4e0d\u53ef\u884c\u65f6\uff0c\u5c31\u9700\u8981\u5b9e\u9a8c\u8bbe\u8ba1\u7b56\u7565\uff0c\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u5bf9\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\u8fdb\u884c\u7f16\u7801\uff0c\u4ece\u800c\u5bf9\u5e72\u9884\u63aa\u65bd\u5bf9\u7cfb\u7edf\u7684\u5f71\u54cd\u8fdb\u884c\u7f16\u7801\uff0c\u5bf9\u4e8e\u66f4\u9ad8\u6548\u5730\u8bc6\u522b\u671f\u671b\u7684\u5e72\u9884\u63aa\u65bd\u81f3\u5173\u91cd\u8981\u3002<\/span><\/p>\n


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\u6587\u7ae0\u5f00\u53d1\u4e86\u4e00\u79cd\u56e0\u679c\u4e3b\u52a8\u5b66\u4e60\u7b56\u7565\uff0c\u4ee5\u8bc6\u522b\u6700\u4f18\u7684\u5e72\u9884\u63aa\u65bd\uff0c\u6700\u4f18\u6027\u7684\u8861\u91cf\u6807\u51c6\u662f\u5206\u5e03\u7684\u5e72\u9884\u540e\u5747\u503c\u4e0e\u671f\u671b\u76ee\u6807\u5747\u503c\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u8be5\u65b9\u6cd5\u91c7\u7528\u56e0\u679c\u6a21\u578b\u7684\u8d1d\u53f6\u65af\u66f4\u65b0\uff0c\u5e76\u4f7f\u7528\u7ecf\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u3001\u5e26\u6709\u56e0\u679c\u4fe1\u606f\u7684\u83b7\u53d6\u51fd\u6570<\/span>\uff08causally informed acquisition function\uff09<\/span>\u6765\u4f18\u5148\u8003\u8651\u5e72\u9884\u63aa\u65bd\u3002<\/span><\/p>\n


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\u56fe1. \u56e0\u679c\u6a21\u578b\u4e2d\u4f18\u5316\u5e72\u9884\u8bbe\u8ba1\u7684\u4e3b\u52a8\u5b66\u4e60\u6846\u67b6\u6982\u89c8\u793a\u610f\u56fe\u3002<\/span><\/section>\n

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\u8fd9\u4e2a\u83b7\u53d6\u51fd\u6570\u53ef\u4ee5\u5728\u5c01\u95ed\u5f62\u5f0f\u4e0b\u8fdb\u884c\u8bc4\u4f30\uff0c\u4ece\u800c\u5b9e\u73b0\u5feb\u901f\u4f18\u5316\uff0c\u5f97\u5230\u7684\u7b97\u6cd5\u5728\u4fe1\u606f\u8bba\u754c\u9650\u548c\u5df2\u77e5\u56e0\u679c\u56fe\u7684\u7ebf\u6027\u56e0\u679c\u6a21\u578b\u7684\u53ef\u8bc1\u660e\u7684\u4e00\u81f4\u6027\u7ed3\u679c\u7684\u7406\u8bba\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u8bbe\u8ba1\u3002\u4f5c\u8005\u5c06\u8fd9\u79cd\u65b9\u6cd5\u5e94\u7528\u4e8e\u5408\u6210\u6570\u636e\u548c\u6765\u81eaPerturb-CITE\u6d4b\u5e8f\u5b9e\u9a8c\u7684\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6570\u636e\uff0c\u4ee5\u8bc6\u522b\u80fd\u591f\u8bf1\u5bfc\u7279\u5b9a\u7ec6\u80de\u72b6\u6001\u8f6c\u53d8\u7684\u6700\u4f18\u6270\u52a8\u3002\u5e26\u6709\u56e0\u679c\u4fe1\u606f\u7684\u83b7\u53d6\u51fd\u6570\u901a\u5e38\u4f18\u4e8e\u73b0\u6709\u7684\u6807\u51c6\uff0c\u5141\u8bb8\u4f7f\u7528\u66f4\u5c11\u4f46\u7ecf\u8fc7\u7cbe\u5fc3\u9009\u62e9\u7684\u6837\u672c\u8fdb\u884c\u6700\u4f18\u5e72\u9884\u8bbe\u8ba1\u3002<\/span><\/p>\n


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\u56fe2. \u6270\u52a8\u5355\u7ec6\u80de\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u96c6\u7684\u7ed3\u679c\u3002<\/span><\/section>\n

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\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u8bfb\u4e66\u4f1a<\/strong><\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/span><\/section>\n
\u8fde\u63a5\u56e0\u679c\u79d1\u5b66\u4e0e\u6df1\u5ea6\u5b66\u4e60\u7684\u6865\u6881\uff1a\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u8bfb\u4e66\u4f1a\u542f\u52a8<\/a><\/span>
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\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/p>\n

\u200d<\/span>1. <\/strong>\u56e0\u679c\u63a8\u7406\u4e0e\u5927\u8bed\u8a00\u6a21\u578b\uff1a\u5f00\u8f9f\u56e0\u679c\u5173\u7cfb\u7684\u65b0\u524d\u6cbf<\/a><\/strong><\/span><\/strong><\/strong><\/strong><\/section>\n
2.<\/strong> \u89e3\u91ca\u5927\u8bed\u8a00\u6a21\u578b\uff1a\u5728 Alpaca \u4e2d\u8bc6\u522b\u56e0\u679c\u673a\u5236<\/a><\/strong><\/span><\/strong><\/section>\n
3. <\/strong><\/strong>\u9762\u5411\u56e0\u679c\u89c4\u5f8b\u7684\u8868\u793a\u5b66\u4e60\u65b0\u65b9\u6cd5\u2014\u2014\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u6700\u65b0\u653b\u7565<\/a><\/strong><\/strong><\/span><\/strong><\/strong><\/section>\n
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4. <\/strong><\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
5. <\/strong><\/strong>\u6210\u4e3a\u96c6\u667aVIP\uff0c\u89e3\u9501\u5168\u7ad9\u8bfe\u7a0b\uff0f\u8bfb\u4e66\u4f1a<\/a><\/span><\/strong><\/strong><\/strong><\/section>\n
6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/section>\n

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\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/strong><\/span><\/strong><\/span><\/strong><\/section>\n

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\u5173\u952e\u8bcd\uff1a\u56e0\u679c\u79d1\u5b66\uff0c\u56e0\u679c\u4e3b\u52a8\u5b66\u4e60\uff0c\u5e72\u9884\u8bbe\u8ba1 \u8bba\u6587\u6807\u9898\uff1aActive learning for optimal intervention design in causal models \u8bba\u6587\u6765\u6e90\uff1aNature Machine Intelligence \u6591\u56fe\u94fe\u63a5\uff1ahttps:\/\/pattern.swarm…<\/p>\n","protected":false},"author":0,"featured_media":45637,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/45643"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=45643"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/45643\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/45637"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=45643"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=45643"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=45643"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=45643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}