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8 changes: 4 additions & 4 deletions 404.html
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Expand Up @@ -656,16 +656,18 @@ <h1>Page not found</h1>
<h2>Latest</h2>
<ul>

<li><a href="/talk/19092023/">&lt;b&gt; &lt;a href=&#34;https://arxiv.org/pdf/2206.00667.pdf&#34;&gt;Our paper &lt;/a&gt; on explaining the sources of bias in machine learning via influence functions has been accepted in FAccT 2023. &lt;/b&gt; Authors: Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel. &lt;br&gt; We combine explainability with fairness in machine learning, where we compute the influence of individual features and the intersectional effect of multiple features on the resulting bias of a classifier on a dataset. This allows us to have a higher granular depiction of sources of bias than earlier methods.</a></li>

<li><a href="/publication/ghosh-phdthesis/">Interpretability and Fairness in Machine Learning: A Formal Methods Approach</a></li>

<li><a href="/talk/19082023/">&lt;b&gt; We have presented a tutorial on &lt;a href=&#34;https://auditing-fairness-tutorial.github.io&#34;&gt;auditing bias in machine learning&lt;/a&gt; in IJCAI 2023. &lt;/b&gt; Presenters: Bishwamittra Ghosh and Debabrota Basu. &lt;br&gt; In this tutorial, we address three questions on bias in machine learning: (i) Choosing a compatible fairness metric based on application context, (ii) Formally quantifying fairness with respect to the compatible metric, and (iii) Explaining the sources of unfairness corresponding to the metric.</a></li>

<li><a href="/project/crane/">Crane</a></li>

<li><a href="/talk/23082023/">&lt;b&gt;We will present our paper &lt;a href=&#34;https://arxiv.org/abs/2306.15693&#34; target=&#34;_blank&#34;&gt;Solving the Identifying Code Set Problem with Grouped Independent Support&lt;/a&gt; this month at &lt;a href=&#34;https://ijcai-23.org/&#34; target=&#34;_blank&#34;&gt;IJCAI 2023&lt;/a&gt;.&lt;/b&gt;&lt;br&gt;We show how reducing an NP-hard problem to a problem in the second order of the polynomial hierarchy helps us to exponentially decrease the encoding size. By leveraging modern solvers that solve problems beyond NP, we can solve much larger problem instances than the former state of the art.&lt;br&gt;If you are attending IJCAI in Macau, please come to our talk on &lt;b&gt;Wednesday 23rd August, at 11:45am&lt;/b&gt; in the &lt;i&gt;CSO: Constraint Programming&lt;/i&gt; session, or join us for the poster session afterwards, from 5pm until 6:30pm. You can also check out &lt;a href=&#34;https://arxiv.org/abs/2306.15693&#34; target=&#34;_blank&#34;&gt;our preprint&lt;/a&gt; or watch &lt;a href=&#34;https://recorder-v3.slideslive.com/?share=85269&amp;s=87084c60-5772-4990-acdf-cd0b9655757d&#34; target=&#34;_blank&#34;&gt;this short video&lt;/a&gt;, which summarises our contribution.&lt;br&gt;Authors: Anna L.D. Latour, Arunabha Sen, Kuldeep S. Meel</a></li>

<li><a href="/publication/lpar23_relnet/">A Fast and Accurate ASP Counting Based Network Reliability Estimator</a></li>

<li><a href="/talk/19072023/">&lt;b&gt;Our work on &lt;a href=&#34;https://link.springer.com/chapter/10.1007/978-3-031-37703-7_7&#34;&gt;Rounding Meets Approximate Model Counting&lt;/a&gt; has been accepted to &lt;a href=&#34;http://www.i-cav.org/2023/&#34;&gt;CAV 2023&lt;/a&gt; and received Distinguished Paper Award.&lt;/b&gt; &lt;br&gt; We round the approximate count of ApproxMC, which allows us to achieve 4$\times$ speedup over the state of the art. &lt;br&gt; Authors: Jiong Yang and Kuldeep S. Meel&lt;br&gt; </a></li>

<li><a href="/publication/icalp23/">Approximate Model Counting: Is SAT Oracle More Powerful than NP Oracle?</a></li>

<li><a href="/publication/mfcs23/">Support Size Estimation: The Power of Conditioning</a></li>
Expand All @@ -674,8 +676,6 @@ <h2>Latest</h2>

<li><a href="/publication/ijcai23_gismo/">Solving the Identifying Code Set Problem with Grouped Independent Support</a></li>

<li><a href="/publication/sat23/">Explaining SAT Solving Using Causal Reasoning</a></li>

</ul>


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66 changes: 1 addition & 65 deletions author/bishwamittra-ghosh/index.xml
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<description>Bishwamittra Ghosh</description>
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<title>How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis</title>
<link>https://meelgroup.github.io/publication/facct23/</link>
<pubDate>Thu, 01 Jun 2023 00:00:00 +0000</pubDate>
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<description></description>
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<title>Efficient Learning of Interpretable Classification Rules</title>
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<pubDate>Tue, 30 Aug 2022 00:00:00 +0000</pubDate>
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<title>Algorithmic Fairness Verification with Graphical Models</title>
<link>https://meelgroup.github.io/publication/aaai22_fvgm/</link>
<pubDate>Mon, 06 Jun 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai22_fvgm/</guid>
<description></description>
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<title>Justicia A Stochastic SAT Approach to Formally Verify Fairness</title>
<link>https://meelgroup.github.io/publication/aaai21_justicia/</link>
<pubDate>Mon, 11 Jan 2021 00:00:00 +0000</pubDate>
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<title>Classification Rules in Relaxed Logical Form</title>
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<title>A MaxSAT-based Framework for Group Testing</title>
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<pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai20/</guid>
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<item>
<title> IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules </title>
<link>https://meelgroup.github.io/publication/aies19/</link>
<pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
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87 changes: 29 additions & 58 deletions author/durgesh-agrawal/index.html
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18 changes: 1 addition & 17 deletions author/durgesh-agrawal/index.xml
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<description>Durgesh Agrawal</description>
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<title>Partition Function Estimation: A Quantitative Study</title>
<link>https://meelgroup.github.io/publication/ijcai21_partition/</link>
<pubDate>Sat, 01 May 2021 00:00:00 +0000</pubDate>
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<title>On the Sparsity of XORs in Approximate Model Counting</title>
<link>https://meelgroup.github.io/publication/sat20abm/</link>
<pubDate>Tue, 19 May 2020 00:00:00 +0000</pubDate>
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76 changes: 18 additions & 58 deletions author/gunjan-kumar/index.html
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18 changes: 17 additions & 1 deletion author/gunjan-kumar/index.xml
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<title>Approximate Model Counting: Is SAT Oracle More Powerful than NP Oracle?</title>
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<title>Support Size Estimation: The Power of Conditioning</title>
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