Two Papers accepted to SAT 2020. 1. The first paper shows that the currently known bounds for sparse hashing are too weak to be used for algorithms such as ApproxMC. Authors: Durgesh Agarwal, Bhavishya and Kuldeep S. Meel 2. The second paper proposes a new phase selection strategy for SAT solvers. Improving SAT solvers is just a very very hard job and we are excited about the improvements that our proposal brings to the world of SAT solving. Authors: Arijit Shaw and Kuldeep S. Meel
Apr 10, 2020
Paper on Sparse Hashing for Approximate Model counting accepted to LICS 2020. Authors: S. Akshay and Kuldeep S. Meel One of the reviews: “Rarely it is that there is a paper that proves a beautiful new theoretical result, explaining and simplifying previous work, and on top of that shows how it can be used to improve state-of-the-art practical algorithms. The paper “Sparse hashing for scalable approximate model counting: theory and practice” achieves exactly that.”
Apr 8, 2020
Paper accepted to LPAR-23. The paper formalizes Induction Models on N extending the classical work of Henkin. Authors: A Dileep, Kuldeep S. Meel and Ammar F. Sabili
Apr 6, 2020
Three Papers accepted at Computer Aided Verification (CAV) 2020 conference. 1. The first paper proposes a new approach that combines sampling+machine learning+MaxSAT to achieve a significant progress in solving Boolean Functional Synthesis. Authors: Priyanka Golia, Subhajit Roy, and Kuldeep S. Meel 2. The second paper builds on our CNF-XOR solving paradigm (BIRD) and as a result, the new versions of ApproxMC and UniGen are faster than ever. Stay tuned for our releases. Authors: Mate Soos, Stephan Gocht, and Kuldeep S. Meel 3. The third paper proposes the first algorithm for approximate MUS counting. Authors: Jaroslav Bendik and Kuldeep S. Meel
Feb 18, 2020
Sampling-based approach for quantittive quantitative verification of Deep Neural Nets. We propose a new attack agnostic metric adversarial hardness to capture the model's robustness: https://arxiv.org/pdf/2002.06864.pdf Authors: Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel and Prateek Saxena.
Our paper on classification rules in relaxed logical form is accepted in ECAI-2020. Authors: Bishwamittra Ghosh, Dmitry Malioutov, and Kuldeep S. Meel
Dec 23, 2019
Our paper on Symmetry breaking and model counting is accepted to TACAS 2020. Authors: Wenxi Wang, Muhammad Usman, Alyas Almaawi, Kaiyuan Wang, Kuldeep S. Meel, and Sarfraz Khurshid
Dec 7, 2019
Two of our group's papers are accepted to accepted as poster presentations with a spotlight talk at StarAI 2020 workshop in AAAI 2020: Our AIES-19 paper on incremental classification rule learning and our CCS-19 paper on quantiatative verification for binarized neural networks.
Nov 11, 2019
Our paper on MaxSAT-based formulation for group testing is accepted in AAAI 2020. Authors: Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett and Kuldeep S. Meel
Our NPAQ framework focused on providing PAC guarantees for verification of Neural Networks is accepted to CCS-19 Quoting reviewer: “This work is pioneering a new technique to solve an incredibly challenging problem, and it shows that smaller problem can be solved. I can live with that, future work can improve computational efficiency.” Authors: Teodora Baluta, Shiqi Shen, Shweta Shinde, Kuldeep S. Meel, Prateek Saxena
Jun 23, 2019
Our paper on interpretable rules expressed as relaxed-CNF is accepted at IJCAI workshop on XAI (Explainable Artificial Intelligence) and DSO (Data Science meets Optimization), 2019. Authors: Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel.
May 15, 2019
Kuldeep recieved notification of the award of NRF Fellowship for AI for the project: Provably Verified and Explainable Probabilistic Reasoning.
May 9, 2019
Two papers accepted to IJCAI.The first paper explores the phase transition behavior of conjunction of cardinality and XOR constraints. Authors: Yash Pote, Saurabh Joshi, Kuldeep Meel. The second paper describes a radically new approach to exact counting wherein we compute estimates that are probabilistically exact! Authors: Shubham Sharma, Kuldeep Meel. Combined with our invited paper on #DNF , this makes 3 papers that we will be presenting at IJCAI.
Apr 22, 2019
Two papers accepted to SAT 2019. The first paper introduces the first version of CrystalBall, a framework intended to allow gazing into the black box of SAT solving. Authors: Kuldeep, Mate Soos, Raghav Kulkarni. The second paper discusses how model counting can be used to analyze explanations provided by tools such as ANCHOR. Authors: Kuldeep, Nina Narodytska, Aditya Shrotri, Alexey Ignatiev, and Joao Marques Silva.