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Equivalence Testing: The Power of Bounded Adaptivity
Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability
An Approximate Skolem Function Counter
Engineering an Exact Pseudo-Boolean Model Counter
Exact ASP Counting with Compact Encodings
Testing Self-Reducible Samplers
<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
Interpretability and Fairness in Machine Learning: A Formal Methods Approach
<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.
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