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    <title>&quot;Bad numbers in the &quot;gzip beats BERT&quot; paper?&quot;</title>
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    <dc:creator>arthegall</dc:creator><description><![CDATA[At some point this comment turned into a github issues thread and then I lost the ... thread, of it.  ]]></description>
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also see comments here: https://gist.github.com/degregat/75949dbf83db3a2c9dfca712cb23bac5]]></description>
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<item rdf:about="https://arxiv.org/abs/1508.01991">
    <title>[1508.01991] Bidirectional LSTM-CRF Models for Sequence Tagging</title>
    <dc:date>2019-12-10T13:12:11+00:00</dc:date>
    <link>https://arxiv.org/abs/1508.01991</link>
    <dc:creator>arthegall</dc:creator><dc:subject>sequence-analysis lstm neural-networks machinelearning arxiv sigma tagging research-article</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:d27ddbfe59ab/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:lstm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sigma"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:tagging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/rflamary/POT">
    <title>rflamary/POT: Python Optimal Transport library</title>
    <dc:date>2019-10-29T09:17:14+00:00</dc:date>
    <link>https://github.com/rflamary/POT</link>
    <dc:creator>arthegall</dc:creator><dc:subject>python library software github optimal-transport machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:196aef0ae130/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:github"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:optimal-transport"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.10871">
    <title>[1910.10871] Preventing Adversarial Use of Datasets through Fair Core-Set Construction</title>
    <dc:date>2019-10-26T15:56:43+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.10871</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:cshalizi adversarial-methods machinelearning arxiv research-article</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:d0ca37d3b3bd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:adversarial-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1602.02201">
    <title>[1602.02201] The Rate-Distortion Risk in Estimation from Compressed Data</title>
    <dc:date>2019-10-26T15:56:15+00:00</dc:date>
    <link>https://arxiv.org/abs/1602.02201</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:? compression machinelearning research-article arxiv</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:b62f3c34e055/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:compression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.semanticscholar.org/paper/Using-Word-Vectors-to-Improve-Word-Alignments-for-Pourdamghani-Ghazvininejad/8f4bf65a6b62cecc6f7b0ab2489a831d9a44a86d">
    <title>[PDF] Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation - Semantic Scholar</title>
    <dc:date>2019-10-26T05:47:48+00:00</dc:date>
    <link>https://www.semanticscholar.org/paper/Using-Word-Vectors-to-Improve-Word-Alignments-for-Pourdamghani-Ghazvininejad/8f4bf65a6b62cecc6f7b0ab2489a831d9a44a86d</link>
    <dc:creator>arthegall</dc:creator><dc:subject>translation nlp word-embeddings machinelearning research-article computational-linguistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:c926c327e04a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:translation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:word-embeddings"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:computational-linguistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.05992">
    <title>[1910.05992] Pathological spectra of the Fisher information metric and its variants in deep neural networks</title>
    <dc:date>2019-10-26T05:47:07+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.05992</link>
    <dc:creator>arthegall</dc:creator><dc:subject>shun-ichi-amari arxiv research-article machinelearning deep-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:67598683e6c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:shun-ichi-amari"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:deep-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1511.05219">
    <title>[1511.05219] How much does your data exploration overfit? Controlling bias via information usage</title>
    <dc:date>2019-10-12T09:41:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1511.05219</link>
    <dc:creator>arthegall</dc:creator><dc:subject>statistics machinelearning via:cshalizi overfitting bias arxiv research-article garden-of-forking-paths</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:2db3b94391b1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:overfitting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bias"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:garden-of-forking-paths"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.04618">
    <title>[1910.04618] Universal Adversarial Perturbation for Text Classification</title>
    <dc:date>2019-10-12T09:40:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.04618</link>
    <dc:creator>arthegall</dc:creator><dc:subject>machinelearning via:cshalizi deep-networks classification nlp text arxiv research-article perturbations</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:0da5fbb4d951/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:deep-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:perturbations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1212.6806">
    <title>[1212.6806] Leveraging Sociological Models for Predictive Analytics</title>
    <dc:date>2019-10-09T14:06:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1212.6806</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[this really reminds me of the bruce bueno de mesquita stuff (who, afaict, is a charlatan ofc) ]]></description>
<dc:subject>sociology arxiv predictions machinelearning networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:a789be833760/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:predictions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1901.08113">
    <title>[1901.08113] Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN</title>
    <dc:date>2019-10-09T13:13:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1901.08113</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity." 

to read]]></description>
<dc:subject>to-read arxiv research-article machinelearning deep-learning graphs networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:248dd068ef2c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:graphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sgfin.github.io/2019/06/19/Causal-Inference-Book-All-DAGs/">
    <title>All the DAGs from Hernan and Robins' Causal Inference Book</title>
    <dc:date>2019-10-07T17:18:16+00:00</dc:date>
    <link>https://sgfin.github.io/2019/06/19/Causal-Inference-Book-All-DAGs/</link>
    <dc:creator>arthegall</dc:creator><dc:subject>pearl causality machinelearning list</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:c0acda49454b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:pearl"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:list"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.isi.edu/projects/nlg/publications">
    <title>NLG Group at USC: Publications</title>
    <dc:date>2019-10-07T12:26:20+00:00</dc:date>
    <link>https://www.isi.edu/projects/nlg/publications</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A lot of these publications are _gold_, including "Translating a Language You Don't Know In the Chinese Room," "Structured Generation of Technical Reading Lists," and "Topical Poetry Generation."  Found my way here through: https://www.isi.edu/natural-language/mt/memorize-random-60.pdf which is interesting in its own right.]]></description>
<dc:subject>nlp structured-text machinelearning publications list poetry translation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:80d374d733ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:structured-text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:publications"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:list"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:poetry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:translation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/116/32/15849">
    <title>Reconciling modern machine-learning practice and the classical bias–variance trade-off | PNAS</title>
    <dc:date>2019-09-14T15:12:46+00:00</dc:date>
    <link>https://www.pnas.org/content/116/32/15849</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Is this "to-be-shot-after-a-fair-trial?" Inquiring minds want to know]]></description>
<dc:subject>hmmm statistics machinelearning review-article bias-variance pnas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:a4cb726efcb4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:hmmm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:review-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bias-variance"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1706.04692">
    <title>[1706.04692] Bias and high-dimensional adjustment in observational studies of peer effects</title>
    <dc:date>2019-07-31T02:44:09+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.04692</link>
    <dc:creator>arthegall</dc:creator><dc:subject>dean-eckles machinelearning observational-studies via:twitter statistics arxiv research-article privacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:6a1f2d44e112/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:observational-studies"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
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</item>
<item rdf:about="https://desfontain.es/privacy/almost-differential-privacy.html">
    <title>Almost differential privacy - Ted is writing things</title>
    <dc:date>2019-06-12T13:48:54+00:00</dc:date>
    <link>https://desfontain.es/privacy/almost-differential-privacy.html</link>
    <dc:creator>arthegall</dc:creator><dc:subject>differential-privacy frank-mcsherry research computerscience machinelearning privacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:c0f4c85f7ee5/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:frank-mcsherry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:computerscience"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:privacy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.02383">
    <title>[1905.02383] Gaussian Differential Privacy</title>
    <dc:date>2019-05-09T11:31:42+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.02383</link>
    <dc:creator>arthegall</dc:creator><dc:subject>aaron-roth differential-privacy via:arsyed composition machinelearning privacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:25769862ca6e/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:differential-privacy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:arsyed"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:privacy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1902.04114">
    <title>[1902.04114] Using Embeddings to Correct for Unobserved Confounding</title>
    <dc:date>2019-02-20T11:12:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.04114</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["We consider causal inference in the presence of unobserved confounding. In particular, we study the case where a proxy is available for the confounder but the proxy has non-iid structure. As one example, the link structure of a social network carries information about its members."]]></description>
<dc:subject>victor-veitch arxiv research-article machinelearning confounding causal-learning causality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:9ba4c0c69695/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:causal-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:causality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1902.06720">
    <title>[1902.06720] Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent</title>
    <dc:date>2019-02-20T11:07:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.06720</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:cshalizi neural-networks deep-learning arxiv research-article machinelearning gradient-descent</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:3311b38c89da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:gradient-descent"/>
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</item>
<item rdf:about="https://dl.acm.org/citation.cfm?doid=1070597">
    <title>Blum, Hartline, &quot;Near-optimal online auctions&quot; (2005)</title>
    <dc:date>2018-09-23T10:49:41+00:00</dc:date>
    <link>https://dl.acm.org/citation.cfm?doid=1070597</link>
    <dc:creator>arthegall</dc:creator><dc:subject>auctions machinelearning randomization research-article online-algorithms</dc:subject>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:online-algorithms"/>
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<item rdf:about="https://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning">
    <title>Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning</title>
    <dc:date>2018-09-20T09:58:04+00:00</dc:date>
    <link>https://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning</link>
    <dc:creator>arthegall</dc:creator><dc:subject>machinelearning via:cshalizi neural-networks randomization research-article nips</dc:subject>
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<item rdf:about="https://arxiv.org/abs/1806.07366">
    <title>[1806.07366] Neural Ordinary Differential Equations</title>
    <dc:date>2018-08-12T09:10:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1806.07366</link>
    <dc:creator>arthegall</dc:creator><dc:subject>arxiv research-article machinelearning neural-networks differential-equations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://arxiv.org/abs/1807.10875">
    <title>[1807.10875] TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing</title>
    <dc:date>2018-08-12T09:10:19+00:00</dc:date>
    <link>https://arxiv.org/abs/1807.10875</link>
    <dc:creator>arthegall</dc:creator><dc:subject>neural-networks debugging machinelearning arxiv research-article ian-goodfellow testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:ian-goodfellow"/>
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</item>
<item rdf:about="https://arxiv.org/abs/1207.5910">
    <title>[1207.5910] Groups acting on Gaussian graphical models</title>
    <dc:date>2018-04-20T15:17:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1207.5910</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[to-do is to go through some of Jan Draisma's other publications]]></description>
<dc:subject>jan-draisma arxiv research-article graphical-models algebraic-statistics machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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</item>
<item rdf:about="https://arxiv.org/abs/1610.09555">
    <title>[1610.09555] TensorLy: Tensor Learning in Python</title>
    <dc:date>2017-04-12T10:44:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1610.09555</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[I'm trying to figure out if "tensor methods" in machine learning means something more than, "GPU-accelerated linear algebra"? ]]></description>
<dc:subject>tensors arxiv machinelearning python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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</item>
<item rdf:about="https://arxiv.org/abs/1406.1837">
    <title>[1406.1837] A Credit Assignment Compiler for Joint Prediction</title>
    <dc:date>2017-04-12T10:44:03+00:00</dc:date>
    <link>https://arxiv.org/abs/1406.1837</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["Learning to Search" ]]></description>
<dc:subject>learning-to-search machinelearning arxiv research-article compiler probabilistic-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:98311fa8e23d/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
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<item rdf:about="https://arxiv.org/abs/1701.03757">
    <title>[1701.03757] Deep Probabilistic Programming</title>
    <dc:date>2017-03-08T04:40:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1701.03757</link>
    <dc:creator>arthegall</dc:creator><dc:subject>david-blei probabilistic-programming deep-learning machinelearning arxiv preprint</dc:subject>
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</item>
<item rdf:about="http://jmlr.org/papers/v15/anandkumar14b.html">
    <title>Tensor Decompositions for Learning Latent Variable Models</title>
    <dc:date>2017-03-08T04:17:58+00:00</dc:date>
    <link>http://jmlr.org/papers/v15/anandkumar14b.html</link>
    <dc:creator>arthegall</dc:creator><dc:subject>machinelearning research-article jmlr tensor-decomposition</dc:subject>
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</item>
<item rdf:about="https://arxiv.org/abs/1611.00783">
    <title>[1611.00783] Preserving Randomness for Adaptive Algorithms</title>
    <dc:date>2016-11-30T18:55:30+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.00783</link>
    <dc:creator>arthegall</dc:creator><dc:subject>adam-klivans arxiv preprint research-article adaptive-algorithms machinelearning</dc:subject>
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</item>
<item rdf:about="https://arxiv.org/abs/1412.6980">
    <title>[1412.6980] Adam: A Method for Stochastic Optimization</title>
    <dc:date>2016-11-30T18:54:33+00:00</dc:date>
    <link>https://arxiv.org/abs/1412.6980</link>
    <dc:creator>arthegall</dc:creator><dc:subject>jimmy-ba arxiv optimization learning machinelearning stochastic-optimization</dc:subject>
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<item rdf:about="http://link.springer.com/article/10.1007/s11238-015-9526-8">
    <title>Minimizing regret in dynamic decision problems | SpringerLink</title>
    <dc:date>2016-11-12T13:23:39+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11238-015-9526-8</link>
    <dc:creator>arthegall</dc:creator><dc:subject>regret machinelearning dynamic-problems research-article joseph-halpern</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:6a8d20d3487b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:dynamic-problems"/>
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</item>
<item rdf:about="https://homes.cs.washington.edu/~marcotcr/blog/lime/">
    <title>LIME - Local Interpretable Model-Agnostic Explanations – Marco Tulio Ribeiro –</title>
    <dc:date>2016-11-12T13:16:18+00:00</dc:date>
    <link>https://homes.cs.washington.edu/~marcotcr/blog/lime/</link>
    <dc:creator>arthegall</dc:creator><dc:subject>lime machinelearning classification interpretation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:bfd32c63af3a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:lime"/>
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</item>
<item rdf:about="https://github.com/marcotcr/lime/">
    <title>marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier</title>
    <dc:date>2016-11-12T13:16:04+00:00</dc:date>
    <link>https://github.com/marcotcr/lime/</link>
    <dc:creator>arthegall</dc:creator><dc:subject>code lime classification machinelearning opensource</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:b7bf7960e8ed/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:code"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:lime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:opensource"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.nips.cc/paper/5677-double-or-nothing-multiplicative-incentive-mechanisms-for-crowdsourcing">
    <title>Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing</title>
    <dc:date>2016-11-03T17:32:52+00:00</dc:date>
    <link>https://papers.nips.cc/paper/5677-double-or-nothing-multiplicative-incentive-mechanisms-for-crowdsourcing</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Actually kind of cool ]]></description>
<dc:subject>incentive-compatible-mechanism nips research-article machinelearning crowdsourcing incentives</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:a8f3027e23ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:incentive-compatible-mechanism"/>
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</item>
<item rdf:about="http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf">
    <title>Glorot, Bengio, &quot;Understanding the difficulty of training deep feedforward neural networks&quot; JMLR (2010)</title>
    <dc:date>2016-11-03T17:24:16+00:00</dc:date>
    <link>http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf</link>
    <dc:creator>arthegall</dc:creator><dc:subject>deep-learning training research-article machinelearning jmlr</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:4b258a89aa9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:deep-learning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:jmlr"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1610.05755">
    <title>Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data</title>
    <dc:date>2016-11-03T17:20:42+00:00</dc:date>
    <link>https://arxiv.org/abs/1610.05755</link>
    <dc:creator>arthegall</dc:creator><dc:subject>arxiv deep-learning machinelearning differential-privacy research-article training-data anonymization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:c4c48a5adbbb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:differential-privacy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:training-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:anonymization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1609.07236">
    <title>[1609.07236] On the (im)possibility of fairness</title>
    <dc:date>2016-11-01T12:27:02+00:00</dc:date>
    <link>https://arxiv.org/abs/1609.07236</link>
    <dc:creator>arthegall</dc:creator><dc:subject>algorithms ethics arxiv machinelearning research-article</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:44e26f8a30de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:algorithms"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cs.cmu.edu/~dsutherl/thesis.pdf">
    <title>Dougal Sutherland, &quot;Scalable, Flexible, and Active Learning on Distributions&quot;</title>
    <dc:date>2016-10-27T16:52:34+00:00</dc:date>
    <link>https://www.cs.cmu.edu/~dsutherl/thesis.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Thesis, CMU ]]></description>
<dc:subject>active-learning machinelearning thesis distributions probabilistic-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:bca2c70a25b4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:active-learning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:thesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:distributions"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://haifengl.github.io/smile/index.html">
    <title>Smile - Statistical Machine Intelligence and Learning Engine</title>
    <dc:date>2016-09-26T14:38:31+00:00</dc:date>
    <link>http://haifengl.github.io/smile/index.html</link>
    <dc:creator>arthegall</dc:creator><dc:subject>smile java machinelearning opensource software scala</dc:subject>
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<item rdf:about="http://arxiv.org/abs/1304.1467">
    <title>[1304.1467] Dimension Independent Matrix Square using MapReduce</title>
    <dc:date>2016-09-26T13:59:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.1467</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["We compute the singular values of an m×n sparse matrix A in a distributed setting, without communication dependence on m, which is useful for very large m."]]></description>
<dc:subject>distributed-computing arxiv research-article similarity machinelearning gunnar-carlsson</dc:subject>
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<item rdf:about="http://www.cs.nyu.edu/~mohri/pub/rej.pdf">
    <title>Cortes et al. &quot;Learning with Rejection&quot;</title>
    <dc:date>2016-09-26T10:06:57+00:00</dc:date>
    <link>http://www.cs.nyu.edu/~mohri/pub/rej.pdf</link>
    <dc:creator>arthegall</dc:creator><dc:subject>machinelearning learning-theory research-article do-no-map work</dc:subject>
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<item rdf:about="https://papers.nips.cc/paper/6004-secure-multi-party-differential-privacy">
    <title>Secure Multi-party Differential Privacy</title>
    <dc:date>2016-09-23T15:30:53+00:00</dc:date>
    <link>https://papers.nips.cc/paper/6004-secure-multi-party-differential-privacy</link>
    <dc:creator>arthegall</dc:creator><dc:subject>nips research-article differential-privacy machinelearning multi-party-computation</dc:subject>
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<item rdf:about="http://dustintran.com/blog/discussion-of-fast-approximate-inference">
    <title>Discussion of &quot;Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing&quot; | Dustin Tran</title>
    <dc:date>2016-09-20T14:46:35+00:00</dc:date>
    <link>http://dustintran.com/blog/discussion-of-fast-approximate-inference</link>
    <dc:creator>arthegall</dc:creator><dc:subject>regression variational-inference expectation-propagation discussion machinelearning andrew-gelman</dc:subject>
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<item rdf:about="http://arxiv.org/abs/1412.4869">
    <title>[1412.4869] Expectation propagation as a way of life</title>
    <dc:date>2016-09-20T14:39:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.4869</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Andrew Gelman has "expectation" and "propagation" tattooed across his knuckles]]></description>
<dc:subject>arxiv machinelearning approximation big-data research-article message-passing expectation-propagation</dc:subject>
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</item>
<item rdf:about="http://courses.cs.washington.edu/courses/cse599s/14sp/scribes.html">
    <title>CSE522: Learning Theory</title>
    <dc:date>2016-09-16T18:18:12+00:00</dc:date>
    <link>http://courses.cs.washington.edu/courses/cse599s/14sp/scribes.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Online learning lecture notes.]]></description>
<dc:subject>online-learning online-algorithms machinelearning class notes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:b7d9484ac0ea/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1609.04120">
    <title>[1609.04120] Private Topic Modeling</title>
    <dc:date>2016-09-15T11:32:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1609.04120</link>
    <dc:creator>arthegall</dc:creator><dc:subject>lda dirichlet-allocation differential-privacy topic-modeling machinelearning arxiv research-article</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:73f22e312cac/</dc:identifier>
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</item>
<item rdf:about="http://arxiv.org/abs/1607.00710">
    <title>[1607.00710] Automatic Generation of Probabilistic Programming from Time Series Data</title>
    <dc:date>2016-08-26T18:06:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1607.00710</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Hmmmm maybe]]></description>
<dc:subject>probabilistic-programming arxiv machinelearning time-series</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:031165b901de/</dc:identifier>
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</item>
<item rdf:about="https://papers.nips.cc/paper/5758-automatic-variational-inference-in-stan.pdf">
    <title>&quot;Automatic Variational Inference in Stan&quot; (NIPS 2016)</title>
    <dc:date>2016-08-26T02:41:36+00:00</dc:date>
    <link>https://papers.nips.cc/paper/5758-automatic-variational-inference-in-stan.pdf</link>
    <dc:creator>arthegall</dc:creator><dc:subject>nips machinelearning stan mcmc automatic-differentiation andrew-gelman variational-methods</dc:subject>
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</item>
<item rdf:about="http://arxiv.org/abs/1607.08456">
    <title>[1607.08456] Kernel functions based on triplet similarity comparisons</title>
    <dc:date>2016-08-25T16:06:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1607.08456</link>
    <dc:creator>arthegall</dc:creator><dc:subject>similarity embedding kernel-methods research-article machinelearning arxiv</dc:subject>
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</item>
<item rdf:about="http://arxiv.org/abs/1605.06197#">
    <title>[1605.06197] Deep Generative Models with Stick-Breaking Priors</title>
    <dc:date>2016-08-25T02:06:53+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.06197#</link>
    <dc:creator>arthegall</dc:creator><dc:subject>stick-breaking dirichlet-process machinelearning research-article arxiv deep-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:6e4cc73094b7/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1607.08845">
    <title>[1607.08845] Limit theorems for the Zig-Zag process</title>
    <dc:date>2016-08-25T02:05:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1607.08845</link>
    <dc:creator>arthegall</dc:creator><dc:subject>sampling mcmc machinelearning research-article arxiv zig-zag</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:53f0caab8992/</dc:identifier>
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</item>
<item rdf:about="http://arxiv.org/abs/1608.06879">
    <title>[1608.06879] AIDE: Fast and Communication Efficient Distributed Optimization</title>
    <dc:date>2016-08-25T02:05:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1608.06879</link>
    <dc:creator>arthegall</dc:creator><dc:subject>distributed-computing optimization alex-smola machinelearning research-article arxiv</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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</item>
<item rdf:about="https://papers.nips.cc/paper/5832-on-elicitation-complexity">
    <title>Rafael Frongillo, Ian Kash, &quot;On Elicitation Complexity&quot;</title>
    <dc:date>2016-08-09T11:03:24+00:00</dc:date>
    <link>https://papers.nips.cc/paper/5832-on-elicitation-complexity</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["a calculus of elicitation" ]]></description>
<dc:subject>rafael-frongillo research-article nips machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:c58004c3fcde/</dc:identifier>
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</item>
<item rdf:about="http://blog.shakirm.com/wp-content/uploads/2015/07/SVDL.pdf">
    <title>&quot;A Statistical View of Machine Learning&quot; (Shakir Mohamed)</title>
    <dc:date>2016-08-09T10:27:10+00:00</dc:date>
    <link>http://blog.shakirm.com/wp-content/uploads/2015/07/SVDL.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A set of notes.  I'm a little confused by the (implied) viewpoint, that "deep" == "any nonlinear, hierarchical model." ]]></description>
<dc:subject>shakir-mohamed deep-learning machinelearning statistics notes via:twitter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:7fdfa0b662ad/</dc:identifier>
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<item rdf:about="http://biorxiv.org/content/early/2016/06/19/059774">
    <title>Voodoo Machine Learning for Clinical Predictions | bioRxiv</title>
    <dc:date>2016-06-28T03:51:57+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2016/06/19/059774</link>
    <dc:creator>arthegall</dc:creator><dc:subject>overfitting cross-validation machinelearning bioarxiv clinical-data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:63c196be6081/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1601.07996">
    <title>[1601.07996] Feature Selection: A Data Perspective</title>
    <dc:date>2016-05-24T11:25:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1601.07996</link>
    <dc:creator>arthegall</dc:creator><dc:subject>feature-selection machinelearning arxiv research-article survey</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:cbaa1c8514cc/</dc:identifier>
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</item>
<item rdf:about="https://papers.nips.cc/paper/5793-the-population-posterior-and-bayesian-modeling-on-streams">
    <title>The Population Posterior and Bayesian Modeling on Streams</title>
    <dc:date>2016-05-24T11:23:56+00:00</dc:date>
    <link>https://papers.nips.cc/paper/5793-the-population-posterior-and-bayesian-modeling-on-streams</link>
    <dc:creator>arthegall</dc:creator><dc:subject>nips machinelearning conference-article bayesian-methods streams</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:8330a462f5a3/</dc:identifier>
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</item>
<item rdf:about="http://arxiv.org/abs/1310.0740v4">
    <title>[1310.0740v4] Pseudo-Marginal Bayesian Inference for Gaussian Processes</title>
    <dc:date>2016-05-24T11:22:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.0740v4</link>
    <dc:creator>arthegall</dc:creator><dc:subject>guassian-processes mark-girolami approximation arxiv research-article machinelearning</dc:subject>
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