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<title>MeelGroup</title>
<link>https://meelgroup.github.io/</link>
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<item>
<title>Locally-Minimal Probabilistic Explanations</title>
<link>https://meelgroup.github.io/publication/ecai24/</link>
<pubDate>Mon, 21 Oct 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ecai24/</guid>
<description></description>
</item>
<item>
<title><b>Our work on lower bounding minimal model count</a> has been accepted to <a href="https://www.iclp24.utdallas.edu/">ICLP 2024</a>.</b> <br> We present two techniques for lower bounding the number of minimal models of a propositional formula. The work has been selected for TPLP journal <br> Authors: Mohimenul Kabir and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/05092024/</link>
<pubDate>Thu, 05 Sep 2024 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/05092024/</guid>
<description></description>
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<item>
<title><b>Our work on Quantum Circuit Mapping Based on Incremental and Parallel SAT Solving</a> has been accepted to <a href="http://satisfiability.org/SAT24/">SAT 2024</a>.</b> <br> We introduce a novel circuit mapping method by combining incremental and parallel solving for Boolean Satisfiability (SAT). We present an innovative SAT encoding for circuit mapping problems, which significantly improves solver-based mapping methods and provides a smooth trade-off between compilation quality and compilation time. <br> Authors: Jiong Yang, Yaroslav A. Kharkov, Yunong Shi, Marijn Heule, and Bruno Dutertre <br> </title>
<link>https://meelgroup.github.io/talk/23052024/</link>
<pubDate>Thu, 23 May 2024 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/23052024/</guid>
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<title><b>Our work on Formally Certified Approximate Model Counting</a> has been accepted to <a href="http://www.i-cav.org/2024/">CAV 2024</a>.</b> <br> We present the first certification framework for approximate model counting with formally verified guarantees on the quality of its output approximation. Our approach combines: (i) a static, once-off, formal proof of the algorithm&apos;s PAC guarantee in the Isabelle/HOL proof assistant; and (ii) dynamic, per-run, verification of ApproxMC&apos;s calls to an external CNF-XOR solver using proof certificates. <br> Authors: Yong Kiam Tan, Jiong Yang, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/26032024/</link>
<pubDate>Tue, 26 Mar 2024 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/26032024/</guid>
<description></description>
</item>
<item>
<title>An Approximate Skolem Function Counter</title>
<link>https://meelgroup.github.io/publication/aaai24_skolemfc/</link>
<pubDate>Wed, 21 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai24_skolemfc/</guid>
<description></description>
</item>
<item>
<title>Auditable Algorithms for Approximate Model Counting</title>
<link>https://meelgroup.github.io/publication/aaai24_auditmc/</link>
<pubDate>Wed, 21 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai24_auditmc/</guid>
<description></description>
</item>
<item>
<title>Engineering an Exact Pseudo-Boolean Model Counter</title>
<link>https://meelgroup.github.io/publication/aaai24_pbcount/</link>
<pubDate>Wed, 21 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai24_pbcount/</guid>
<description></description>
</item>
<item>
<title>Exact ASP Counting with Compact Encodings</title>
<link>https://meelgroup.github.io/publication/aaai24_sharpasp/</link>
<pubDate>Wed, 21 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai24_sharpasp/</guid>
<description></description>
</item>
<item>
<title>Testing Self-Reducible Samplers</title>
<link>https://meelgroup.github.io/publication/aaai24_cubeprobe/</link>
<pubDate>Wed, 21 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai24_cubeprobe/</guid>
<description></description>
</item>
<item>
<title>Equivalence Testing: The Power of Bounded Adaptivity</title>
<link>https://meelgroup.github.io/publication/aistats24/</link>
<pubDate>Sat, 20 Jan 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aistats24/</guid>
<description></description>
</item>
<item>
<title>Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability</title>
<link>https://meelgroup.github.io/publication/icdt24/</link>
<pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/icdt24/</guid>
<description></description>
</item>
<item>
<title><b>Five Papers accepted to <a href="https://aaai.org/aaai-conference/">AAAI 2024</a>.</b> <br> 1. The first paper is Auditable Algorithms for Approximate Model Counting <br> &nbsp;&nbsp;&nbsp; Authors: S. Akshay, Supratik Chakraborty and Kuldeep S. Meel</br> 2. The second paper is An Approximate Skolem Function Counter <br> &nbsp;&nbsp;&nbsp; Authors: Arijit Shaw, Brendan Juba and Kuldeep S. Meel</br> 3. The third paper is Exact ASP Counting with Compact Encodings <br> &nbsp;&nbsp;&nbsp; Authors: Mohimenul Kabir, Supratik Chakraborty and Kuldeep S. Meel</br> 4. The fourth paper is Testing Self-Reducible Samplers <br> &nbsp;&nbsp;&nbsp; Authors: Rishiraj Bhattacharyya, Sourav Chakraborty, Yash Pote, Uddalok Sarkar and Sayantan Sen</br> 5. The fifth paper is Engineering an Exact Pseudo-Boolean Model Counter <br> &nbsp;&nbsp;&nbsp; Authors: Suwei Yang and Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/14122023/</link>
<pubDate>Thu, 14 Dec 2023 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/14122023/</guid>
<description></description>
</item>
<item>
<title>Functional Synthesis via Formal Methods and Machine Learning</title>
<link>https://meelgroup.github.io/publication/golia-phdthesis/</link>
<pubDate>Mon, 25 Sep 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/golia-phdthesis/</guid>
<description></description>
</item>
<item>
<title>Interpretability and Fairness in Machine Learning: A Formal Methods Approach</title>
<link>https://meelgroup.github.io/publication/ghosh-phdthesis/</link>
<pubDate>Fri, 01 Sep 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ghosh-phdthesis/</guid>
<description></description>
</item>
<item>
<title><b> We have presented a tutorial on <a href="https://auditing-fairness-tutorial.github.io">auditing bias in machine learning</a> in IJCAI 2023. </b> Presenters: Bishwamittra Ghosh and Debabrota Basu. <br> 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.</title>
<link>https://meelgroup.github.io/talk/19082023/</link>
<pubDate>Sat, 19 Aug 2023 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/19082023/</guid>
<description></description>
</item>
<item>
<title>Crane</title>
<link>https://meelgroup.github.io/project/crane/</link>
<pubDate>Sun, 13 Aug 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/project/crane/</guid>
<description><p>First-order model counting (FOMC) is a computational problem that asks to count the models of a sentence in finite-domain first-order logic. In this paper, we argue that the capabilities of FOMC algorithms to date are limited by their inability to express many types of recursive computations. To enable such computations, we relax the restrictions that typically accompany domain recursion and generalise the circuits used to express a solution to an FOMC problem to directed graphs that may contain cycles. To this end, we adapt the most well-established (weighted) FOMC algorithm ForcLift to work with such graphs and introduce new compilation rules that can create cycle-inducing edges that encode recursive function calls. These improvements allow the algorithm to find efficient solutions to counting problems that were previously beyond its reach, including those that cannot be solved efficiently by any other exact FOMC algorithm. We end with a few conjectures on what classes of instances could be domain-liftable as a result.</p>
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<item>
<title><b>We will present our paper <a href="https://arxiv.org/abs/2306.15693" target="_blank">Solving the Identifying Code Set Problem with Grouped Independent Support</a> this month at <a href="https://ijcai-23.org/" target="_blank">IJCAI 2023</a>.</b><br>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.<br>If you are attending IJCAI in Macau, please come to our talk on <b>Wednesday 23rd August, at 11:45am</b> in the <i>CSO: Constraint Programming</i> session, or join us for the poster session afterwards, from 5pm until 6:30pm. You can also check out <a href="https://arxiv.org/abs/2306.15693" target="_blank">our preprint</a> or watch <a href="https://recorder-v3.slideslive.com/?share=85269&s=87084c60-5772-4990-acdf-cd0b9655757d" target="_blank">this short video</a>, which summarises our contribution.<br>Authors: Anna L.D. Latour, Arunabha Sen, Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/23082023/</link>
<pubDate>Thu, 03 Aug 2023 12:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/23082023/</guid>
<description></description>
</item>
<item>
<title>A Fast and Accurate ASP Counting Based Network Reliability Estimator</title>
<link>https://meelgroup.github.io/publication/lpar23_relnet/</link>
<pubDate>Fri, 28 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/lpar23_relnet/</guid>
<description></description>
</item>
<item>
<title><b>Our work on <a href="https://link.springer.com/chapter/10.1007/978-3-031-37703-7_7">Rounding Meets Approximate Model Counting</a> has been accepted to <a href="http://www.i-cav.org/2023/">CAV 2023</a> and received Distinguished Paper Award.</b> <br> We round the approximate count of ApproxMC, which allows us to achieve 4$\times$ speedup over the state of the art. <br> Authors: Jiong Yang and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/19072023/</link>
<pubDate>Wed, 19 Jul 2023 13:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/19072023/</guid>
<description></description>
</item>
<item>
<title>Approximate Model Counting: Is SAT Oracle More Powerful than NP Oracle?</title>
<link>https://meelgroup.github.io/publication/icalp23/</link>
<pubDate>Tue, 04 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/icalp23/</guid>
<description></description>
</item>
<item>
<title>Support Size Estimation: The Power of Conditioning</title>
<link>https://meelgroup.github.io/publication/mfcs23/</link>
<pubDate>Tue, 04 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/mfcs23/</guid>
<description></description>
</item>
<item>
<title>On Approximating Total Variation Distance</title>
<link>https://meelgroup.github.io/publication/ijcai23_totvardist/</link>
<pubDate>Sat, 01 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ijcai23_totvardist/</guid>
<description></description>
</item>
<item>
<title>Solving the Identifying Code Set Problem with Grouped Independent Support</title>
<link>https://meelgroup.github.io/publication/ijcai23_gismo/</link>
<pubDate>Sat, 01 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ijcai23_gismo/</guid>
<description></description>
</item>
<item>
<title>Explaining SAT Solving Using Causal Reasoning</title>
<link>https://meelgroup.github.io/publication/sat23/</link>
<pubDate>Tue, 27 Jun 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/sat23/</guid>
<description></description>
</item>
<item>
<title>Rounding Meets Approximate Model Counting</title>
<link>https://meelgroup.github.io/publication/cav23/</link>
<pubDate>Tue, 27 Jun 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cav23/</guid>
<description></description>
</item>
<item>
<title>Probabilistic Query Evaluation: The Combined FPRAS Landscape</title>
<link>https://meelgroup.github.io/publication/pods23/</link>
<pubDate>Sun, 18 Jun 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/pods23/</guid>
<description></description>
</item>
<item>
<title><b>Our paper <a href="https://meelgroup.github.io/files/publications/kr23_fomc.pdf">Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures</a> has been accepted to <a href="https://kr.org/KR2023/">KR 2023</a>.</b><br>We extend the first-order model counting algorithm ForcLift with new compilation rules, enabling it to discover recursive solutions to previously-unsolved instances.<br>Authors: Paulius Dilkas, Vaishak Belle<br></title>
<link>https://meelgroup.github.io/talk/09062023/</link>
<pubDate>Fri, 09 Jun 2023 12:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/09062023/</guid>
<description></description>
</item>
<item>
<title>Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures</title>
<link>https://meelgroup.github.io/publication/kr23/</link>
<pubDate>Fri, 09 Jun 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/kr23/</guid>
<description></description>
</item>
<item>
<title>Scalable Probabilistic Routes</title>
<link>https://meelgroup.github.io/publication/lpar23_probroute/</link>
<pubDate>Sun, 04 Jun 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/lpar23_probroute/</guid>
<description></description>
</item>
<item>
<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>
<guid>https://meelgroup.github.io/publication/facct23/</guid>
<description></description>
</item>
<item>
<title>Generating Random Instances of Weighted Model Counting: An Empirical Analysis with Varying Primal Treewidth</title>
<link>https://meelgroup.github.io/publication/cpaior23/</link>
<pubDate>Mon, 29 May 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cpaior23/</guid>
<description></description>
</item>
<item>
<title>On Tackling Explanation Redundancy in Decision Trees</title>
<link>https://meelgroup.github.io/publication/ijcai-23-iim/</link>
<pubDate>Mon, 01 May 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ijcai-23-iim/</guid>
<description></description>
</item>
<item>
<title>Synthesis with Explicit Dependencies</title>
<link>https://meelgroup.github.io/publication/date23/</link>
<pubDate>Mon, 17 Apr 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/date23/</guid>
<description></description>
</item>
<item>
<title><b> <a href="https://arxiv.org/pdf/2206.00667.pdf">Our paper </a> on explaining the sources of bias in machine learning via influence functions has been accepted in FAccT 2023. </b> Authors: Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel. <br> 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.</title>
<link>https://meelgroup.github.io/talk/08042023/</link>
<pubDate>Sat, 08 Apr 2023 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/08042023/</guid>
<description></description>
</item>
<item>
<title>On computing probabilistic abductive explanations</title>
<link>https://meelgroup.github.io/publication/ijar-23-ihincm/</link>
<pubDate>Sat, 01 Apr 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ijar-23-ihincm/</guid>
<description></description>
</item>
<item>
<title><b>Our paper <a href="https://meelgroup.github.io/files/publications/cpaior23_wmc.pdf">Generating Random Instances of Weighted Model Counting: An Empirical Analysis with Varying Primal Treewidth</a> has been accepted to <a href="https://sites.google.com/view/cpaior2023/">CPAIOR 2023</a>.</b><br>We introduce a random model and use it to discover some critical differences among weighted model counting algorithms.<br>Authors: Paulius Dilkas<br></title>
<link>https://meelgroup.github.io/talk/25022023/</link>
<pubDate>Sat, 25 Feb 2023 12:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/25022023/</guid>
<description></description>
</item>
<item>
<title>Solving Explainability Queries with Quantification: The Case of Feature Relevancy</title>
<link>https://meelgroup.github.io/publication/aaai-23-him/</link>
<pubDate>Wed, 01 Feb 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai-23-him/</guid>
<description></description>
</item>
<item>
<title>Testing of Horn Samplers</title>
<link>https://meelgroup.github.io/publication/aistats23/</link>
<pubDate>Fri, 20 Jan 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aistats23/</guid>
<description></description>
</item>
<item>
<title>Constraint Optimization over Semirings</title>
<link>https://meelgroup.github.io/publication/aaai23_semirings/</link>
<pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai23_semirings/</guid>
<description></description>
</item>
<item>
<title>Fast Converging Anytime Model Counting</title>
<link>https://meelgroup.github.io/publication/aaai23_anytimemc/</link>
<pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai23_anytimemc/</guid>
<description></description>
</item>
<item>
<title><b>Our paper on Synthesis with Explicit Dependencies has been accepted to <a href="https://www.date-conference.com/">DATE 2023</a></b>. Moreover, it has also received the <b>best paper award nomination</b>.<br>We present an approach that combines advances in machine learning with automated reasoning for efficiently synthesizing functions with explicit dependencies.<br>Authors: Priyanka Golia, Subhajit Roy, and Kuldeep S. Meel<br></title>
<link>https://meelgroup.github.io/talk/16112022/</link>
<pubDate>Wed, 16 Nov 2022 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/16112022/</guid>
<description></description>
</item>
<item>
<title><b>Our paper <a href="https://meelgroup.github.io/files/publications/NeurIPS22_ScalTest.pdf">On Scalable Testing of Samplers</a> is accepted to <a href="https://nips.cc/">NeurIPS 2022</a>.</b><br> Authors: Yash Pote and Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/14092022/</link>
<pubDate>Sun, 06 Nov 2022 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/14092022/</guid>
<description></description>
</item>
<item>
<title>On Scalable Testing of Samplers</title>
<link>https://meelgroup.github.io/publication/neurips22/</link>
<pubDate>Wed, 14 Sep 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/neurips22/</guid>
<description></description>
</item>
<item>
<title>Efficient Learning of Interpretable Classification Rules</title>
<link>https://meelgroup.github.io/publication/jair22/</link>
<pubDate>Tue, 30 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/jair22/</guid>
<description></description>
</item>
<item>
<title>INC: A Scalable Incremental Weighted Sampler</title>
<link>https://meelgroup.github.io/publication/fmcad22/</link>
<pubDate>Mon, 22 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/fmcad22/</guid>
<description></description>
</item>
<item>
<title>Distinct Elements in Streams: An Algorithm for the (Text) Book</title>
<link>https://meelgroup.github.io/publication/esa22/</link>
<pubDate>Mon, 01 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/esa22/</guid>
<description></description>
</item>
<item>
<title>Projected Model Counting: Beyond Independent Support</title>
<link>https://meelgroup.github.io/publication/atva22/</link>
<pubDate>Mon, 01 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/atva22/</guid>
<description></description>
</item>
<item>
<title><b>Our work on <a href="files/publications/ATVA22.pdf">Projected Model Counting: Beyond Independent Support</a> has been accepted to <a href="https://atva-conference.org/2022/">ATVA 2022</a>.</b> <br> We show that we can identify a set of variables, called <em> upper bound support (UBS) </em>, that is not necessarily a subset of sampling set, and yet counting models projected on UBS guarantees an upper bound of the projected model count. <br> Authors: Jiong Yang, Supratik Chakraborty, and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/04072022/</link>
<pubDate>Mon, 04 Jul 2022 13:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/04072022/</guid>
<description></description>
</item>
<item>
<title>Counting, Sampling, and Synthesis: The Quest for Scalability</title>
<link>https://meelgroup.github.io/publication/ijcai22/</link>
<pubDate>Fri, 01 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ijcai22/</guid>
<description></description>
</item>
<item>
<title>On Almost-Uniform Generation of SAT Solutions: The power of 3-wise independent hashing</title>
<link>https://meelgroup.github.io/publication/lics22/</link>
<pubDate>Thu, 23 Jun 2022 00:00:01 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/lics22/</guid>
<description></description>
</item>
<item>
<title>Estimation of the Size of Union of Delphic Sets: Achieving Independence from Stream Size</title>
<link>https://meelgroup.github.io/publication/pods22/</link>
<pubDate>Wed, 22 Jun 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/pods22/</guid>
<description></description>
</item>
<item>
<title>ApproxASP – A Scalable Approximate Answer Set Counter</title>
<link>https://meelgroup.github.io/publication/aaai22-approxasp/</link>
<pubDate>Thu, 09 Jun 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai22-approxasp/</guid>
<description></description>
</item>
<item>
<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>
</item>
<item>
<title>Model Counting Meets Distinct Elements in a Data Stream</title>
<link>https://meelgroup.github.io/publication/sigmod22/</link>
<pubDate>Wed, 01 Jun 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/sigmod22/</guid>
<description></description>
</item>
<item>
<title><b>Our work on A Scalable Shannon Entropy Estimator is accepted to <a href="http://i-cav.org/2022/">CAV-22</a>.</b> <br> We propose the first efficient algorithmic technique to estimate the Shannon entropy of a specification with PAC-style guarantees, i.e., the computed estimate is guaranteed to lie within a (1 ± ε)-factor of the ground truth with confidence at least 1−δ. <br>Authors: Priyanka Golia, Brendan Juba, Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/09052022/</link>
<pubDate>Mon, 09 May 2022 13:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/09052022/</guid>
<description></description>
</item>
<item>
<title>A Scalable Shannon Entropy Estimator</title>
<link>https://meelgroup.github.io/publication/cav22/</link>
<pubDate>Mon, 09 May 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cav22/</guid>
<description></description>
</item>
<item>
<title>On Quantitative Testing of Samplers</title>
<link>https://meelgroup.github.io/publication/cp22/</link>
<pubDate>Mon, 09 May 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cp22/</guid>
<description></description>
</item>
<item>
<title><b>Our work On Quantitative Testing of Samplers has been accepted to <a href="https://cp2022.a4cp.org">CP-22</a>.</b> <br> We design a computational hardness-based tester, called ScalBarbarik that provides a qunatitative way to analysis the quality of a sampler.<br> Authors: Mate Soos, Priyanka Golia, Sourav Chakraborty, Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/08052022/</link>
<pubDate>Sun, 08 May 2022 13:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/08052022/</guid>
<description></description>
</item>
<item>
<title>A Scalable t-wise Coverage Estimator</title>
<link>https://meelgroup.github.io/publication/icse22/</link>
<pubDate>Sun, 01 May 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/icse22/</guid>
<description></description>
</item>
<item>
<title>Constraint-Driven Explanations of Black-Box ML Models</title>
<link>https://meelgroup.github.io/publication/aaai22-explanations/</link>
<pubDate>Tue, 01 Feb 2022 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai22-explanations/</guid>
<description></description>
</item>
<item>
<title><b>Our paper on "Efficient Learning of Interpretable Classification Rules" is accepted to <a href="https://www.jair.org/index.php/jair">JAIR</a> journal.</b><br> Authors: Bishwamittra Ghosh, Dmitry Malioutov and Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/21122021/</link>
<pubDate>Tue, 21 Dec 2021 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/21122021/</guid>
<description></description>
</item>
<item>
<title><b>Our paper “A Scalable t-wise Coverage Estimator” is accepted to. <a href="https://conf.researchr.org/home/icse-2022"> ICSE 2022</a>.</b><br> Authors: Eduard Baranov, Sourav Chakraborty, Axel Legay, Kuldeep S. Meel, and N.V. Vinodchandran</title>
<link>https://meelgroup.github.io/talk/03122021/</link>
<pubDate>Fri, 03 Dec 2021 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/03122021/</guid>
<description></description>
</item>
<item>
<title><b>Three Papers accepted to <a href="https://aaai.org/Conferences/AAAI-22/">AAAI 2022</a>.</b> <br>1. The first paper is on Algorithmic Fairness Verification with Graphical Models. <br> &nbsp;&nbsp;&nbsp; Authors: Bishwamittra Ghosh, Debabrota Basu and Kuldeep S. Meel</br> 2. The second paper is ApproxASP - A Scalable Approximate Answer Set Counter. <br>&nbsp;&nbsp;&nbsp; Authors: Mohimenul Kabir , Flavio Everardo, Ankit Shukla, Johannes K. Fichte, Markus Hecher and Kuldeep Meel</br> 3. The third paper is on Constraint-Driven Explanations for Black Box ML Models. <br> &nbsp;&nbsp;&nbsp; Authors: Aditya Shrotri, Nina Narodytska, Alexey Ignatiev, Joao Marques-Silva, Kuldeep S. Meel and Moshe Vardi</title>
<link>https://meelgroup.github.io/talk/02122021/</link>
<pubDate>Thu, 02 Dec 2021 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/02122021/</guid>
<description></description>
</item>
<item>
<title>Gaussian Elimination Meets Maximum Satisfiability</title>
<link>https://meelgroup.github.io/publication/kr21/</link>
<pubDate>Tue, 09 Nov 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/kr21/</guid>
<description></description>
</item>
<item>
<title>Testing Probabilistic Circuits</title>
<link>https://meelgroup.github.io/publication/neurips21_teq/</link>
<pubDate>Sun, 07 Nov 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/neurips21_teq/</guid>
<description></description>
</item>
<item>
<title><b>Our paper on <a href="https://meelgroup.github.io/files/publications/NeurIPS21_PCtest.pdf">Testing of Probabilistic Cirucits</a> is accepted to <a href="https://nips.cc/">NeurIPS 2021</a>.</b><br> Authors: Yash Pote and Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/07112021/</link>
<pubDate>Sun, 07 Nov 2021 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/07112021/</guid>
<description></description>
</item>
<item>
<title><b>Congratulations to Teodora Baluta for winning the <a href="https://research.google/outreach/phd-fellowship/recipients/?category=2021">Google PhD Fellowship</a>.</b></title>
<link>https://meelgroup.github.io/talk/24092021/</link>
<pubDate>Sat, 30 Oct 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/24092021/</guid>
<description></description>
</item>
<item>
<title>Engineering an Efficient PB-XOR Solver</title>
<link>https://meelgroup.github.io/publication/cp21/</link>
<pubDate>Wed, 28 Jul 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cp21/</guid>
<description></description>
</item>
<item>
<title><b>Our paper on <a href="https://meelgroup.github.io/files/publications/CP21.pdf">Engineering an Efficient PB-XOR Solver</a> is accepted to <a href="https://cp2021.a4cp.org/">CP 2021</a>.</b><br> Authors: Jiong Yang and Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/28072021/</link>
<pubDate>Wed, 28 Jul 2021 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/28072021/</guid>
<description></description>
</item>
<item>
<title><b>Our paper Engineering an Efficient Boolean Functional Synthesis Engine has been accepted to <a href="https://www.iccad.com/index.php">ICCAD 2021</a>.</b> <br> The work addresses scalability barriers faced by the current state-of-the-art synthesis techniques. We propose four algorithmic improvements for a data-driven framework for functional synthesis.<br> Authors: Priyanka Golia, Friedrich Slivovsky, Subhajit Roy, Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/14072021/</link>
<pubDate>Wed, 14 Jul 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/14072021/</guid>
<description></description>
</item>
<item>
<title>Engineering an Efficient Boolean Functional Synthesis Engine</title>
<link>https://meelgroup.github.io/publication/iccad21/</link>
<pubDate>Tue, 13 Jul 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/iccad21/</guid>
<description></description>
</item>
<item>
<title><b>Our paper Designing Samplers is Easy: The Boon of Testers has been accepted to <a href="https://fmcad.org/FMCAD21/">FMCAD 2021</a>.</b> <br> Our Sampler not only passes the tests of Barbarik but also leads to significant performance improvements for real-world instances. <br> Authors: Priyanka Golia, Mate Soos, Sourav Chakraborty, Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/09072021/</link>
<pubDate>Fri, 09 Jul 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/09072021/</guid>
<description></description>
</item>
<item>
<title>Designing Samplers is Easy: The Boon of Testers</title>
<link>https://meelgroup.github.io/publication/fmcad21/</link>
<pubDate>Fri, 09 Jul 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/fmcad21/</guid>
<description></description>
</item>
<item>
<title><b>Our work on MaxSAT augmented with Gauss Jordan Elimination is accepted to <a href="https://kr2021.kbsg.rwth-aachen.de/">KR2021</a>.</b> <br> Authors: Mate Soos and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/15062021/</link>
<pubDate>Tue, 15 Jun 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/15062021/</guid>
<description></description>
</item>
<item>
<title><b>Our work on GPU-based Parallel SAT solving is accepted to <a href="https://www.iiia.csic.es/sat2021/program/">SAT 2021</a>.</b><br> Authors: Nicolas Prevot, Mate Soos, and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/04052021/</link>
<pubDate>Tue, 04 May 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/04052021/</guid>
<description></description>
</item>
<item>
<title>Counting Minimal Unsatisfiable Subsets</title>
<link>https://meelgroup.github.io/publication/cav21_mus/</link>
<pubDate>Sat, 01 May 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cav21_mus/</guid>
<description></description>
</item>
<item>
<title>Leveraging GPUs for Effective Clause Sharing in Parallel SAT Solving</title>
<link>https://meelgroup.github.io/publication/sat21_gpu/</link>
<pubDate>Sat, 01 May 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/sat21_gpu/</guid>
<description></description>
</item>
<item>
<title>On the Usefulness of Linear Modular Arithmetic in Constraint Programming</title>
<link>https://meelgroup.github.io/publication/cpaior21_linmodar/</link>
<pubDate>Sat, 01 May 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cpaior21_linmodar/</guid>
<description></description>
</item>
<item>
<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>
<guid>https://meelgroup.github.io/publication/ijcai21_partition/</guid>
<description></description>
</item>
<item>
<title><b>Our paper on Program Synthesis as Dependency Quantified Formula Modulo Theory has been accepted to <a href="https://ijcai-21.org/">IJCAI 2021</a>.</b> <br>We show that theory-constrained synthesis can be reduced DQF(T), i.e., to the problem of finding a witness of a dependency quantified formula modulo theory. <br> Authors: Priyanka Golia, Subhajit Roy, Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/30042021/</link>
<pubDate>Fri, 30 Apr 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/30042021/</guid>
<description></description>
</item>
<item>
<title>Program Synthesis as Dependency Quantified Formula Modulo Theory</title>
<link>https://meelgroup.github.io/publication/ijcai21_programsynthesis/</link>
<pubDate>Fri, 30 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/ijcai21_programsynthesis/</guid>
<description></description>
</item>
<item>
<title><b>Our work on Counting Minimal Unsatisfiable Subsets is accepted to <a href="http://i-cav.org/2021/">CAV 2021</a>.</b> <br> Authors: Jaroslav Bendik and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/21042021/</link>
<pubDate>Wed, 21 Apr 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/21042021/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep presented a talk on the rise of model counting at the <a href="https://ml-logic-seminar.github.io/">Data and Knowledge Seminar at Oxford</a>.</title>
<link>https://meelgroup.github.io/talk/20042021/</link>
<pubDate>Tue, 20 Apr 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/20042021/</guid>
<description></description>
</item>
<item>
<title><b>Our paper Partition Function Estimation: A Quantitative Study has been accepted to <a href="https://ijcai-21.org/">IJCAI 2021 Survey Track</a>.</b> <br>This paper presents a survey of 17 partition function estimation techniques and a rigorous empirical study of their behavior across an extensive set of benchmarks.<br> Authors: Durgesh Agrawal, Yash Pote, and Kuldeep S. Meel<br> </title>
<link>https://meelgroup.github.io/talk/19042021/</link>
<pubDate>Mon, 19 Apr 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/19042021/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep gave a talk on Formal Methods+Machine Learning at <a href="https://cci.usc.edu/index.php/2021/04/13/4863/">CCI MHI Seminars at USC</a>.</b></title>
<link>https://meelgroup.github.io/talk/15042021/</link>
<pubDate>Thu, 15 Apr 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/15042021/</guid>
<description></description>
</item>
<item>
<title><b>Our work on computation of union of sets in streaming model is accepted to <a href="https://2021.sigmod.org/calls_papers_pods_research.shtml">PODS-21</a>.</b> <br> Authors: Kuldeep S. Meel, N.V.Vinodchandran, and Sourav Chakraborty <br> </title>
<link>https://meelgroup.github.io/talk/02042021/</link>
<pubDate>Fri, 02 Apr 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/02042021/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep gave a talk on Formal Methods+Machine Learning at the <a href="https://madpl.cs.wisc.edu/pl-seminar/">PL Seminar at University of Wisconsin</a>.</b></title>
<link>https://meelgroup.github.io/talk/24032021/</link>
<pubDate>Wed, 24 Mar 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/24032021/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep gave a talk on Formal Methods+Machine Learning at <a href="https://ml-logic-seminar.github.io/">Waterloo&apos;s ML+Logic Seminar</a>.</b></title>
<link>https://meelgroup.github.io/talk/15032021/</link>
<pubDate>Mon, 15 Mar 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/15032021/</guid>
<description></description>
</item>
<item>
<title><b>Our work on Linear Modular Arithmetic in CP is accepted to <a href="https://cpaior2021.dbai.tuwien.ac.at/">CPAIOR-21</a>.</b> <br> Authors: Gilles Pesant, Kuldeep S. Meel, and Mahshid Mohammadalitajrishi<br> </title>
<link>https://meelgroup.github.io/talk/13032021/</link>
<pubDate>Sat, 13 Mar 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/13032021/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep will present a talk on synthesis at the <a href="https://citris-uc.org/event/cpar-seminar-series-kuldeep-meel/">CITRIS People and Robotics seminar</a>.</b></title>
<link>https://meelgroup.github.io/talk/01032021/</link>
<pubDate>Mon, 01 Mar 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/01032021/</guid>
<description></description>
</item>
<item>
<title>Estimating the Size of Unions of Sets in Streaming Models</title>
<link>https://meelgroup.github.io/publication/pods21_union/</link>
<pubDate>Thu, 18 Feb 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/pods21_union/</guid>
<description></description>
</item>
<item>
<title>Model Counting meets F0 Estimation</title>
<link>https://meelgroup.github.io/publication/pods21_f0/</link>
<pubDate>Thu, 18 Feb 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/pods21_f0/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep will be giving an in-depth talk on the rise of approximate model counting at Simons Workshop on Beyond Satisfiability. <a href="https://simons.berkeley.edu/talks/tbd-257">Recorded video </a>.</title>
<link>https://meelgroup.github.io/talk/09022021/</link>
<pubDate>Tue, 09 Feb 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/09022021/</guid>
<description></description>
</item>
<item>
<title><b>Kuldeep will be giving an introductory talk on <a href="https://simons.berkeley.edu/talks/pseudo-boolean-solving-and-optimization">Approximate Counting and Sampling </a> at Simons Bootcamp for Program on Satisfiability.</title>
<link>https://meelgroup.github.io/talk/05022021/</link>
<pubDate>Fri, 05 Feb 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/05022021/</guid>
<description></description>
</item>
<item>
<title>Scalable Quantitative Verification For Deep Neural Networks</title>
<link>https://meelgroup.github.io/publication/icse21/</link>
<pubDate>Wed, 13 Jan 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/icse21/</guid>
<description></description>
</item>
<item>
<title>Counting Maximal Satisfiable Subsets</title>
<link>https://meelgroup.github.io/publication/aaai21_mss/</link>
<pubDate>Mon, 11 Jan 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai21_mss/</guid>
<description></description>
</item>
<item>
<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>
<guid>https://meelgroup.github.io/publication/aaai21_justicia/</guid>
<description></description>
</item>
<item>
<title>Predicting Forest Fire Using Remote Sensing Data And Machine Learning</title>
<link>https://meelgroup.github.io/publication/aaai21_forest/</link>
<pubDate>Mon, 11 Jan 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai21_forest/</guid>
<description></description>
</item>
<item>
<title>Symmetric Component Caching for Model Counting on Structured Instances</title>
<link>https://meelgroup.github.io/publication/aaai21_symganak/</link>
<pubDate>Mon, 11 Jan 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai21_symganak/</guid>
<description></description>
</item>
<item>
<title>The Power of Literal Equivalence in Model Counting</title>
<link>https://meelgroup.github.io/publication/aaai21_exactmc/</link>
<pubDate>Mon, 11 Jan 2021 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/aaai21_exactmc/</guid>
<description></description>
</item>
<item>
<title><b>Join, from the comfort of your home, our tutorial on <a href="https://alexeyignatiev.github.io/ijcai20-tutorial/">Logic-Enabled Verification and Explanation of ML Models</a> at IJCAI-21.</title>
<link>https://meelgroup.github.io/talk/08012021/</link>
<pubDate>Fri, 08 Jan 2021 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/08012021/</guid>
<description></description>
</item>
<item>
<title><b>Our paper on scalable quantitative verification for deep neural networks has been accepted to <a href="https://conf.researchr.org/home/icse-2021">ICSE 2021</a>.</b> <br>We give a sampling-based approach for quantifying properties for deep neural networks and an attack-agnostic metric called adversarial hardness to capture a model&apos;s robustness. <br> Authors: Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel and Prateek Saxena <br> </title>
<link>https://meelgroup.github.io/talk/17122020/</link>
<pubDate>Thu, 17 Dec 2020 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/17122020/</guid>
<description></description>
</item>
<item>
<title><b>We will be presenting three papers at <a href="https://nips.cc/virtual/2020/public/cal_main.html">NeurIPS-20</a>.</b> <br>1. The [first paper](https://arxiv.org/abs/2002.05378) focuses on efficient distance approximation in high dimension distributions. We propose an amazingly simple method that can compute L1 distance with rigorous guarantees. Come [Poster Session 5](https://neurips.cc/virtual/2020/public/poster_a8acc28734d4fe90ea24353d901ae678.html) on Thursday. <br> &nbsp;&nbsp;&nbsp; Joint work with A. Bhattacharya, S. Gayen, and N.V. Vinodchandran <br>2. The [second paper](https://arxiv.org/abs/2010.12918) provides the first scalable method to test samplers in practice. Barbarik can now test samplers that sample from log-linear models. If you propose a sampling technique but can&apos;t prove its correctness, you can now use Barbarik to check its quality. Just the way we use testing for our software. ([Poster](https://t.co/zRZVsPZNka?amp=1)) <br> &nbsp;&nbsp;&nbsp; Joint work with S. Chakraborty and Y. Pote <br>3. The [third paper](https://arxiv.org/abs/2010.10724) seeks to tame discrete integration with the boon of dimensionality (Yes, the boon not the curse). We extended our IJCAI-15&apos;s work of weighted to unweighted counting (thereby increasing the dimensionality) to handle rational weights. ([Poster](https://t.co/J4rYz0j8Vh?amp=1)) <br> &nbsp;&nbsp;&nbsp; Joint work with J.M. Dudek and D. Fried</title>
<link>https://meelgroup.github.io/talk/05122020/</link>
<pubDate>Sat, 05 Dec 2020 14:15:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/05122020/</guid>
<description></description>
</item>
<item>
<title><b>All the five papers from our group were accepted to <a href="https://aaai.org/Conferences/AAAI-21/">AAAI 2021</a>.</b> <br>1. Justicia: A Stochastic SAT Approach to Formally Verify Fairness <br> &nbsp;&nbsp;&nbsp; Authors: Bishwamittra Ghosh, Debabrota Basu, and Kuldeep S Meel <br>2. Predicting Forest Fire Using Remote Sensing Data And Machine Learning <br> &nbsp;&nbsp;&nbsp; Authors: Suwei Yang, Massimo Lupascu, and Kuldeep S Meel <br>3. Symmetric Component Caching for Model Counting on Structured Instances <br> &nbsp;&nbsp;&nbsp; Authors: Timothy van Bremen, Vincent Derkinderen, Shubham Sharma, Subhajit Roy, and Kuldeep S Meel <br> 4. Counting Maximal Satisfiable Subsets <br> &nbsp;&nbsp;&nbsp; Authors: Jaroslav Bendik and Kuldeep S. Meel <br>5. The Power of Literal Equivalence in Model Counting <br> &nbsp;&nbsp;&nbsp; Authors: Yong Lai, Kuldeep S Meel, and Roland Yap</title>
<link>https://meelgroup.github.io/talk/02122020/</link>
<pubDate>Wed, 02 Dec 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/02122020/</guid>
<description></description>
</item>
<item>
<title>Baital: An Adaptive Weighted Sampling Approach for Improved t-wise Coverage</title>
<link>https://meelgroup.github.io/publication/fse20/</link>
<pubDate>Sun, 08 Nov 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/fse20/</guid>
<description></description>
</item>
<item>
<title>Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning</title>
<link>https://meelgroup.github.io/publication/neurips20_approx/</link>
<pubDate>Fri, 25 Sep 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/neurips20_approx/</guid>
<description></description>
</item>
<item>
<title>On Testing of Samplers</title>
<link>https://meelgroup.github.io/publication/neurips20_testing/</link>
<pubDate>Fri, 25 Sep 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/neurips20_testing/</guid>
<description></description>
</item>
<item>
<title>Taming Discrete Integration via the Boon of Dimensionality</title>
<link>https://meelgroup.github.io/publication/neurips20_taming/</link>
<pubDate>Fri, 25 Sep 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/neurips20_taming/</guid>
<description></description>
</item>
<item>
<title><b>Three Papers accepted to <a href="https://nips.cc/">NeurIPS 2020</a>.</b> <br>1. The first paper on <a href="https://arxiv.org/abs/2010.12918">On Testing of Samplers</a>. <br> &nbsp;&nbsp;&nbsp; Authors: Kuldeep S Meel, Yash Pralhad Pote and Sourav Chakraborty</br> 2. The second paper on <a href="https://arxiv.org/abs/2010.10724">Taming Discrete Integration via the Boon of Dimensionality</a>. <br>&nbsp;&nbsp;&nbsp; Authors: Jeffrey M. Dudek, Dror Fried and Kuldeep S. Meel</br> 3. The third paper on <a href="https://arxiv.org/abs/2002.05378">Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning</a>. <br> &nbsp;&nbsp;&nbsp; Authors: Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel and N. V. Vinodchandran</title>
<link>https://meelgroup.github.io/talk/25092020/</link>
<pubDate>Fri, 25 Sep 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/25092020/</guid>
<description></description>
</item>
<item>
<title><b>Our Paper on Model Counting meets F0 Estimation (public version forthcoming!) is accepted to <a href="https://2021.sigmod.org/">PODS 2021</a>.</b><br> Authors: Arnab Bhattacharyya, Kuldeep Meel, A. Pavan and N.V. Vinodchandran</title>
<link>https://meelgroup.github.io/talk/18092020/</link>
<pubDate>Fri, 18 Sep 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/18092020/</guid>
<description></description>
</item>
<item>
<title><b> We [presented](https://www.youtube.com/watch?v=vCc6jpOm04Y&feature=youtu.be) our work on [Phase Transition Behaviour in Knowledge Compilation](https://arxiv.org/abs/2007.10400) at <a href="https://cp2020.a4cp.org/">CP 2020</a>.</b></title>
<link>https://meelgroup.github.io/talk/10092020/</link>
<pubDate>Thu, 10 Sep 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/10092020/</guid>
<description></description>
</item>
<item>
<title><b> We presented three papers at [CAV 2020](http://i-cav.org/2020/). </b> <br> 1. The first paper builds on our CNF-XOR solving paradigm (BIRD) and as a result, the new versions of [ApproxMC and UniGen](https://www.cs.toronto.edu/~meel/Papers/cav20-sgm.pdf) are faster than ever. <br> 2. The second paper proposes the first algorithm for [approximate MUS counting](https://www.cs.toronto.edu/~meel/Papers/cav20-bm.pdf). <br> 3. The third one proposes a data-driven approach for [Boolean functional synthesis](https://www.cs.toronto.edu/~meel/Papers/cav20-grm.pdf), which works at the intersection of constrained sampling, machine learning and automated reasoning.</title>
<link>https://meelgroup.github.io/talk/22072020/</link>
<pubDate>Wed, 22 Jul 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/22072020/</guid>
<description></description>
</item>
<item>
<title><b>We have released the source code of [Manthan](https://github.com/meelgroup/manthan).</b></title>
<link>https://meelgroup.github.io/talk/07072020/</link>
<pubDate>Mon, 13 Jul 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/07072020/</guid>
<description></description>
</item>
<item>
<title>Phase Transition Behaviour in Knowledge Compilation</title>
<link>https://meelgroup.github.io/publication/cp20/</link>
<pubDate>Wed, 08 Jul 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cp20/</guid>
<description></description>
</item>
<item>
<title><b>Our Paper on <a href="https://arxiv.org/abs/2007.10400">Phase Transition Behaviour in Knowledge Compilation</a> is accepted to <a href="https://cp2020.a4cp.org/">CP 2020</a>.</b><br> Authors: Rahul Gupta, Subhajit Roy and Kuldeep S. Meel</title>
<link>https://meelgroup.github.io/talk/08072020/</link>
<pubDate>Wed, 08 Jul 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/08072020/</guid>
<description></description>
</item>
<item>
<title><b>We have released [ApproxMC 4](https://github.com/meelgroup/approxmc).</b></title>
<link>https://meelgroup.github.io/talk/13072020/</link>
<pubDate>Tue, 07 Jul 2020 00:00:00 +0530</pubDate>
<guid>https://meelgroup.github.io/talk/13072020/</guid>
<description></description>
</item>
<item>
<title>Designing New Phase Selection Heuristics</title>
<link>https://meelgroup.github.io/publication/sat20sm/</link>
<pubDate>Tue, 19 May 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/sat20sm/</guid>
<description></description>
</item>
<item>
<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>
<guid>https://meelgroup.github.io/publication/sat20abm/</guid>
<description></description>
</item>
<item>
<title>Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice</title>
<link>https://meelgroup.github.io/publication/lics-20-am/</link>
<pubDate>Mon, 18 May 2020 00:00:01 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/lics-20-am/</guid>
<description></description>
</item>
<item>
<title>Approximate Counting of Minimal Unsatisfiable Subsets</title>
<link>https://meelgroup.github.io/publication/cav20bm/</link>
<pubDate>Mon, 18 May 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cav20bm/</guid>
<description></description>
</item>
<item>
<title>Manthan: A Data-Driven Approach for Boolean Function Synthesis</title>
<link>https://meelgroup.github.io/publication/cav20_manthan/</link>
<pubDate>Mon, 18 May 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cav20_manthan/</guid>
<description></description>
</item>
<item>
<title>Tinted, Detached, and Lazy CNF-XOR solving and its Applications to Counting and Sampling</title>
<link>https://meelgroup.github.io/publication/cav20sgm/</link>
<pubDate>Mon, 18 May 2020 00:00:00 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/cav20sgm/</guid>
<description></description>
</item>
<item>
<title>Induction Models on N</title>
<link>https://meelgroup.github.io/publication/lpar20dms/</link>
<pubDate>Sat, 16 May 2020 00:00:01 +0000</pubDate>
<guid>https://meelgroup.github.io/publication/lpar20dms/</guid>
<description></description>
</item>
<item>