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- <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's PAC guarantee in the Isabelle/HOL proof assistant; and (ii) dynamic, per-run, verification of ApproxMC'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>
- An Approximate Skolem Function Counter
- Auditable Algorithms for Approximate Model Counting
- Engineering an Exact Pseudo-Boolean Model Counter
- Exact ASP Counting with Compact Encodings
- Testing Self-Reducible Samplers
- Equivalence Testing: The Power of Bounded Adaptivity
- Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability
- <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> Authors: S. Akshay, Supratik Chakraborty and Kuldeep S. Meel</br> 2. The second paper is An Approximate Skolem Function Counter <br> Authors: Arijit Shaw, Brendan Juba and Kuldeep S. Meel</br> 3. The third paper is Exact ASP Counting with Compact Encodings <br> Authors: Mohimenul Kabir, Supratik Chakraborty and Kuldeep S. Meel</br> 4. The fourth paper is Testing Self-Reducible Samplers <br> 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> Authors: Suwei Yang and Kuldeep S. Meel
- Functional Synthesis via Formal Methods and Machine Learning