<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://proceedings.mlr.press/r16/feed.xml" rel="self" type="application/atom+xml" /><link href="https://proceedings.mlr.press/r16/" rel="alternate" type="text/html" /><updated>2026-10-04T22:57:13+00:00</updated><id>https://proceedings.mlr.press/r16/feed.xml</id><title type="html">Proceedings of Machine Learning Research</title><subtitle>Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence
  Held in Monterey, California, USA on 06-10 August 2018

Published as Reissue 16 by the Proceedings of Machine Learning Research on 04 October 2026.

Volume Edited by:
  Amir Globerson
  Ricardo Silva

Series Editors:
  Tegan Emerson
  Hoel Kervadec
  Neil D. Lawrence
</subtitle><author><name>PMLR</name></author><entry><title type="html">Learning Time Series Segmentation Models from Temporally Imprecise Labels</title><link href="https://proceedings.mlr.press/r16/adams18a.html" rel="alternate" type="text/html" title="Learning Time Series Segmentation Models from Temporally Imprecise Labels" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/adams18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/adams18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Roy&quot;, &quot;family&quot;=&gt;&quot;Adams&quot;}, {&quot;given&quot;=&gt;&quot;Benjamin M.&quot;, &quot;family&quot;=&gt;&quot;Marlin&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving</title><link href="https://proceedings.mlr.press/r16/aditya18a.html" rel="alternate" type="text/html" title="Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/aditya18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/aditya18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Somak&quot;, &quot;family&quot;=&gt;&quot;Aditya&quot;}, {&quot;given&quot;=&gt;&quot;Yezhou&quot;, &quot;family&quot;=&gt;&quot;Yang&quot;}, {&quot;given&quot;=&gt;&quot;Chitta&quot;, &quot;family&quot;=&gt;&quot;Baral&quot;}, {&quot;given&quot;=&gt;&quot;Yiannis&quot;, &quot;family&quot;=&gt;&quot;Aloimonos&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain Environments</title><link href="https://proceedings.mlr.press/r16/agrawal18a.html" rel="alternate" type="text/html" title="Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain Environments" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/agrawal18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/agrawal18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Pritee&quot;, &quot;family&quot;=&gt;&quot;Agrawal&quot;}, {&quot;given&quot;=&gt;&quot;Pradeep&quot;, &quot;family&quot;=&gt;&quot;Varakantham&quot;}, {&quot;given&quot;=&gt;&quot;William&quot;, &quot;family&quot;=&gt;&quot;Yeoh&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning</title><link href="https://proceedings.mlr.press/r16/atzmon18a.html" rel="alternate" type="text/html" title="Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/atzmon18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/atzmon18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Yuval&quot;, &quot;family&quot;=&gt;&quot;Atzmon&quot;}, {&quot;given&quot;=&gt;&quot;Gal&quot;, &quot;family&quot;=&gt;&quot;Chechik&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling</title><link href="https://proceedings.mlr.press/r16/babichev18a.html" rel="alternate" type="text/html" title="Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/babichev18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/babichev18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Dmitry&quot;, &quot;family&quot;=&gt;&quot;Babichev&quot;}, {&quot;given&quot;=&gt;&quot;Francis&quot;, &quot;family&quot;=&gt;&quot;Bach&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Sylvester Normalizing Flows for Variational Inference</title><link href="https://proceedings.mlr.press/r16/berg18a.html" rel="alternate" type="text/html" title="Sylvester Normalizing Flows for Variational Inference" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/berg18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/berg18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Rianne van den&quot;, &quot;family&quot;=&gt;&quot;Berg&quot;}, {&quot;given&quot;=&gt;&quot;Leonard&quot;, &quot;family&quot;=&gt;&quot;Hasenclever&quot;}, {&quot;given&quot;=&gt;&quot;Jakub&quot;, &quot;family&quot;=&gt;&quot;Tomczak&quot;}, {&quot;given&quot;=&gt;&quot;Max&quot;, &quot;family&quot;=&gt;&quot;Welling&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Sparse-Matrix Belief Propagation</title><link href="https://proceedings.mlr.press/r16/bixler18a.html" rel="alternate" type="text/html" title="Sparse-Matrix Belief Propagation" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/bixler18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/bixler18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Reid&quot;, &quot;family&quot;=&gt;&quot;Bixler&quot;}, {&quot;given&quot;=&gt;&quot;Bert&quot;, &quot;family&quot;=&gt;&quot;Huang&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks</title><link href="https://proceedings.mlr.press/r16/bloem-reddy18a.html" rel="alternate" type="text/html" title="Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/bloem-reddy18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/bloem-reddy18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Benjamin&quot;, &quot;family&quot;=&gt;&quot;Bloem-Reddy&quot;}, {&quot;given&quot;=&gt;&quot;Adam&quot;, &quot;family&quot;=&gt;&quot;Foster&quot;}, {&quot;given&quot;=&gt;&quot;Emile&quot;, &quot;family&quot;=&gt;&quot;Mathieu&quot;}, {&quot;given&quot;=&gt;&quot;Yee Whye&quot;, &quot;family&quot;=&gt;&quot;Teh&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Causal Discovery in the Presence of Measurement Error</title><link href="https://proceedings.mlr.press/r16/blom18a.html" rel="alternate" type="text/html" title="Causal Discovery in the Presence of Measurement Error" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/blom18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/blom18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Tineke&quot;, &quot;family&quot;=&gt;&quot;Blom&quot;}, {&quot;given&quot;=&gt;&quot;Anna&quot;, &quot;family&quot;=&gt;&quot;Klimovskaia&quot;}, {&quot;given&quot;=&gt;&quot;Sara&quot;, &quot;family&quot;=&gt;&quot;Magliacane&quot;}, {&quot;given&quot;=&gt;&quot;Joris M.&quot;, &quot;family&quot;=&gt;&quot;Mooij&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry><entry><title type="html">Abstraction Sampling in Graphical Models</title><link href="https://proceedings.mlr.press/r16/broka18a.html" rel="alternate" type="text/html" title="Abstraction Sampling in Graphical Models" /><published>2018-08-06T00:00:00+00:00</published><updated>2018-08-06T00:00:00+00:00</updated><id>https://proceedings.mlr.press/r16/broka18a</id><content type="html" xml:base="https://proceedings.mlr.press/r16/broka18a.html"><![CDATA[]]></content><author><name>[{&quot;given&quot;=&gt;&quot;Filjor&quot;, &quot;family&quot;=&gt;&quot;Broka&quot;}, {&quot;given&quot;=&gt;&quot;Rina&quot;, &quot;family&quot;=&gt;&quot;Dechter&quot;}, {&quot;given&quot;=&gt;&quot;Alexander&quot;, &quot;family&quot;=&gt;&quot;Ihler&quot;}, {&quot;given&quot;=&gt;&quot;Kalev&quot;, &quot;family&quot;=&gt;&quot;Kask&quot;}]</name></author><summary type="html"><![CDATA[]]></summary></entry></feed>