82 citations · 369 across the 63 of their papers we have counts for
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Fast Converging Anytime Model Counting
Yong Lai, Kuldeep S. Meel, Roland H. C. Yap
Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due to the intrinsic hardness of model…
Support Size Estimation: The Power of Conditioning
Diptarka Chakraborty, Gunjan Kumar, Kuldeep S. Meel
We consider the problem of estimating the support size of a distribution . Our investigations are pursued through the lens of distribution testing and seek to understand the pow…
A Scalable Shannon Entropy Estimator
Priyanka Golia, Brendan Juba, Kuldeep S. Meel
We revisit the well-studied problem of estimating the Shannon entropy of a probability distribution, now given access to a probability-revealing conditional sampling oracle. In thi…
On Approximating Total Variation Distance
Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel +3
Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV d…
CCDD: A Tractable Representation for Model Counting and Uniform Sampling
Yong Lai, Kuldeep S. Meel, Roland H. C. Yap
Knowledge compilation concerns with the compilation of representation languages to target languages supporting a wide range of tractable operations arising from diverse areas of co…