no code implementations • 13 Aug 2024 • Arijit Shaw, Kuldeep S. Meel

To this end, we surveyed 11 application domains and collected an aggregate of 2262 benchmarks from these domains.

1 code implementation • 19 Jul 2024 • Anna L. D. Latour, Arunabha Sen, Kaustav Basu, Chenyang Zhou, Kuldeep S. Meel

In the context of satellite monitoring of the earth, we can assume that the surface of the earth is divided into a set of regions.

no code implementations • 17 Jun 2024 • Yong Kiam Tan, Jiong Yang, Mate Soos, Magnus O. Myreen, Kuldeep S. Meel

Approximate model counting is the task of approximating the number of solutions to an input Boolean formula.

1 code implementation • 19 Dec 2023 • Yacine Izza, Kuldeep S. Meel, Joao Marques-Silva

Explainable Artificial Intelligence (XAI) is widely regarding as a cornerstone of trustworthy AI.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

1 code implementation • 19 Dec 2023 • Suwei Yang, Kuldeep S. Meel

Our work opens up several avenues for future work in the context of model counting for PB formulas, such as the development of preprocessing techniques and exploration of approaches other than knowledge compilation.

no code implementations • 19 Dec 2023 • Kuldeep S. Meel, Supratik Chakraborty, S. Akshay

Since $n$ is often large, we ask if the count of variables in the certificate can be reduced -- a crucial question for potential implementation.

no code implementations • 17 Sep 2023 • Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models.

no code implementations • 19 Jun 2023 • Suwei Yang, Victor C. Liang, Kuldeep S. Meel

In this work, our contributions are two-fold: first, we introduce a relaxed encoding that uses a linear number of variables with respect to the number of vertices in a road network graph to significantly reduce the size of resultant decision diagrams.

no code implementations • 9 Jun 2023 • Jiong Yang, Arijit Shaw, Teodora Baluta, Mate Soos, Kuldeep S. Meel

Subsequently, given a query such as whether a clause with low literals blocks distance (LBD) has a higher clause utility, CausalSAT calculates the causal effect of LBD on clause utility and provides an answer to the question.

1 code implementation • 16 May 2023 • Jiong Yang, Kuldeep S. Meel

The problem of model counting, also known as #SAT, is to compute the number of models or satisfying assignments of a given Boolean formula $F$.

no code implementations • 25 Jan 2023 • Priyanka Golia, Subhajit Roy, Kuldeep S. Meel

In QBF, an existentially quantified variable is allowed to depend on all universally quantified variables in its scope.

1 code implementation • 19 Dec 2022 • 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.

1 code implementation • 1 Jun 2022 • Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel

In this paper, we aim to quantify the influence of different features in a dataset on the bias of a classifier.

no code implementations • 14 May 2022 • Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel

The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable.

1 code implementation • 21 Feb 2022 • 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 computer science.

no code implementations • 18 Oct 2021 • Mate Soos, Kuldeep S. Meel

Given a Boolean formula $\varphi$ over the set of variables $X$ and a projection set $\mathcal{P} \subseteq X$, a subset of variables $\mathcal{I}$ is independent support of $\mathcal{P}$ if two solutions agree on $\mathcal{I}$, then they also agree on $\mathcal{P}$.

no code implementations • 18 Oct 2021 • Jiong Yang, Supratik Chakraborty, Kuldeep S. Meel

We show that in several such cases, we can identify a set of variables, called upper bound support (UBS), that is not necessarily a subset of the projection set, and yet counting models projected on UBS guarantees an upper bound of the true projected model count.

1 code implementation • 20 Sep 2021 • Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel

In recent years, machine learning (ML) algorithms have been deployed in safety-critical and high-stake decision-making, where the fairness of algorithms is of paramount importance.

1 code implementation • 12 Aug 2021 • Priyanka Golia, Friedrich Slivovsky, Subhajit Roy, Kuldeep S. Meel

In this paper, we propose four algorithmic improvements for a data-driven framework for functional synthesis: using a dependency-driven multi-classifier to learn candidate function, extracting uniquely defined functions by interpolation, variables retention, and using lexicographic MaxSAT to repair candidates.

1 code implementation • 19 May 2021 • Priyanka Golia, Subhajit Roy, Kuldeep S. Meel

Over the past decade, syntax-guided synthesis (SyGuS) has emerged as a dominant approach for program synthesis where in addition to the specification $\varphi$, the end-user also specifies a grammar $L$ to aid the underlying synthesis engine.

no code implementations • 6 Jan 2021 • Suwei Yang, Massimo Lupascu, Kuldeep S. Meel

The results support our claim that machine-learning based approaches can lead to reliable and cost-effective forest fire prediction systems.

no code implementations • 1 Jan 2021 • Aditya Aniruddha Shrotri, Nina Narodytska, Alexey Ignatiev, Joao Marques-Silva, Kuldeep S. Meel, Moshe Vardi

Modern machine learning techniques have enjoyed widespread success, but are plagued by lack of transparency in their decision making, which has led to the emergence of the field of explainable AI.

1 code implementation • NeurIPS 2020 • Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel

The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting.

1 code implementation • 14 Sep 2020 • Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel

We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds.

no code implementations • 20 Jul 2020 • Rahul Gupta, Subhajit Roy, Kuldeep S. Meel

The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of modern SAT solvers.

2 code implementations • 14 May 2020 • Priyanka Golia, Subhajit Roy, Kuldeep S. Meel

On an extensive and rigorous evaluation over 609 benchmarks, we demonstrate that Manthan significantly improves upon the current state of the art, solving 356 benchmarks in comparison to 280, which is the most solved by a state of the art technique; thereby, we demonstrate an increase of 76 benchmarks over the current state of the art.

1 code implementation • 30 Apr 2020 • Kuldeep S. Meel, S. Akshay

In this paper, we address this challenge from theoretical and practical perspectives.

no code implementations • 17 Feb 2020 • Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel, Prateek Saxena

But these existing techniques provide either scalability to large networks or formal guarantees, not both.

no code implementations • NeurIPS 2020 • Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran

We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions.

no code implementations • 7 Jan 2020 • Bishwamittra Ghosh, Kuldeep S. Meel

While MLIC was shown to achieve accuracy similar to that of other state of the art black-box classifiers while generating small interpretable CNF formulas, the runtime performance of MLIC is significantly lagging and renders approach unusable in practice.

1 code implementation • 22 Oct 2019 • Yash Pote, Saurabh Joshi, Kuldeep S. Meel

The runtime performance of modern SAT solvers is deeply connected to the phase transition behavior of CNF formulas.

1 code implementation • NeurIPS 2019 • Yaqi Xie, Ziwei Xu, Mohan S. Kankanhalli, Kuldeep S. Meel, Harold Soh

Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.

no code implementations • 25 Jun 2019 • Teodora Baluta, Shiqi Shen, Shweta Shinde, Kuldeep S. Meel, Prateek Saxena

Neural networks are increasingly employed in safety-critical domains.

1 code implementation • 5 Dec 2018 • Dmitry Malioutov, Kuldeep S. Meel

The wide adoption of machine learning approaches in the industry, government, medicine and science has renewed the interest in interpretable machine learning: many decisions are too important to be delegated to black-box techniques such as deep neural networks or kernel SVMs.

no code implementations • 6 Jun 2018 • Kuldeep S. Meel

In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints.

no code implementations • 14 Oct 2017 • Kuldeep S. Meel, Aditya A. Shrotri, Moshe Y. Vardi

When the constraints are expressed as DNF formulas, Monte Carlo-based techniques have been shown to provide a fully polynomial randomized approximation scheme (FPRAS).

no code implementations • 21 Dec 2015 • Kuldeep S. Meel, Moshe Vardi, Supratik Chakraborty, Daniel J. Fremont, Sanjit A. Seshia, Dror Fried, Alexander Ivrii, Sharad Malik

Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification.

1 code implementation • 24 Nov 2015 • Supratik Chakraborty, Kuldeep S. Meel, Rakesh Mistry, Moshe Y. Vardi

Techniques based on bit-level (or Boolean) hash functions require these problems to be propositionalized, making it impossible to leverage the remarkable progress made in SMT (Satisfiability Modulo Theory) solvers that can reason directly over words (or bit-vectors).

no code implementations • 11 Apr 2014 • Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi

We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems.

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