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ORCIDKuldeep S. Meel
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- affiliation: University of Toronto, Canada
- affiliation (former): National University of Singapore, School of Computing
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2020 – today
- 2024
[j11]Jaroslav Bendík
, Kuldeep S. Meel:
Hashing-based approximate counting of minimal unsatisfiable subsets. Formal Methods Syst. Des. 63(1): 5-39 (2024)- [j10]Aduri Pavan, Sourav Chakraborty, N. V. Vinodchandran, Kuldeep S. Meel:
On the Feasibility of Forgetting in Data Streams. Proc. ACM Manag. Data 2(2): 102 (2024) - [j9]Kuldeep S. Meel, Sourav Chakraborty, Umang Mathur:
A faster FPRAS for #NFA. Proc. ACM Manag. Data 2(2): 112 (2024) - [j8]Eduard Baranov, Sourav Chakraborty, Axel Legay, Kuldeep S. Meel, N. Variyam Vinodchandran:
A Scalable $t$t-Wise Coverage Estimator: Algorithms and Applications. IEEE Trans. Software Eng. 50(8): 2021-2039 (2024) - [c109]Arijit Shaw, Brendan Juba, Kuldeep S. Meel:
An Approximate Skolem Function Counter. AAAI 2024: 8108-8116 - [c108]Suwei Yang, Kuldeep S. Meel:
Engineering an Exact Pseudo-Boolean Model Counter. AAAI 2024: 8200-8208 - [c107]Mohimenul Kabir, Supratik Chakraborty, Kuldeep S. Meel:
Exact ASP Counting with Compact Encodings. AAAI 2024: 10571-10580 - [c106]Kuldeep S. Meel, Supratik Chakraborty, S. Akshay:
Auditable Algorithms for Approximate Model Counting. AAAI 2024: 10654-10661 - [c105]Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Equivalence Testing: The Power of Bounded Adaptivity. AISTATS 2024: 3592-3600 - [c104]Mridul Nandi, N. V. Vinodchandran, Arijit Ghosh, Kuldeep S. Meel, Soumit Pal, Sourav Chakraborty:
Improved Streaming Algorithm for the Klee's Measure Problem and Generalizations. APPROX/RANDOM 2024: 26:1-26:21 - [c103]Yong Kiam Tan, Jiong Yang, Mate Soos, Magnus O. Myreen, Kuldeep S. Meel:
Formally Certified Approximate Model Counting. CAV (1) 2024: 153-177 - [c102]Yacine Izza, Kuldeep S. Meel, João Marques-Silva:
Locally-Minimal Probabilistic Explanations. ECAI 2024: 1092-1099 - [c101]Antoine Amarilli, Timothy van Bremen, Kuldeep S. Meel:
Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability. ICDT 2024: 15:1-15:20 - [c100]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran:
Total Variation Distance Meets Probabilistic Inference. ICML 2024 - [c99]Arijit Shaw, Kuldeep S. Meel:
CSB: A Counting and Sampling Tool for Bit-vectors. SMT@CAV 2024: 36-43 - [i76]Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Equivalence Testing: The Power of Bounded Adaptivity. CoRR abs/2403.04230 (2024) - [i75]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran:
Total Variation Distance for Product Distributions is #P-Complete. CoRR abs/2405.08255 (2024) - [i74]Yong Kiam Tan, Jiong Yang, Mate Soos, Magnus O. Myreen, Kuldeep S. Meel:
Formally Certified Approximate Model Counting. CoRR abs/2406.11414 (2024) - [i73]Kuldeep S. Meel, Alexis de Colnet:
An FPRAS for #nFBDD. CoRR abs/2406.16515 (2024) - [i72]Kuldeep S. Meel, Alexis de Colnet:
#CFG and #DNNF admit FPRAS. CoRR abs/2406.18224 (2024) - [i71]Mohimenul Kabir, Kuldeep S. Meel:
On Lower Bounding Minimal Model Count. CoRR abs/2407.09744 (2024) - [i70]Anna L. D. Latour, Arunabha Sen, Kaustav Basu, Chenyang Zhou, Kuldeep S. Meel:
The Cardinality of Identifying Code Sets for Soccer Ball Graph with Application to Remote Sensing. CoRR abs/2407.14120 (2024) - [i69]Mate Soos, Uddalok Sarkar, Divesh Aggarwal, Sourav Chakraborty, Kuldeep S. Meel, Maciej Obremski:
Engineering an Efficient Approximate DNF-Counter. CoRR abs/2407.19946 (2024) - [i68]Arijit Shaw, Kuldeep S. Meel:
Model Counting in the Wild. CoRR abs/2408.07059 (2024) - [i67]S. Akshay, Bernd Finkbeiner, Kuldeep S. Meel, Ruzica Piskac, Arijit Shaw:
Automated Synthesis: Functional, Reactive and Beyond (Dagstuhl Seminar 24171). Dagstuhl Reports 14(4): 85-107 (2024) - 2023
- [j7]A. Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel:
Model Counting Meets Distinct Elements. Commun. ACM 66(9): 95-102 (2023) - [j6]A. Pavan, N. Variyam Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel:
Model Counting Meets F0 Estimation. ACM Trans. Database Syst. 48(3): 7:1-7:28 (2023) - [c98]Yong Lai, Kuldeep S. Meel, Roland H. C. Yap:
Fast Converging Anytime Model Counting. AAAI 2023: 4025-4034 - [c97]Aduri Pavan, Kuldeep S. Meel, N. V. Vinodchandran, Arnab Bhattacharyya:
Constraint Optimization over Semirings. AAAI 2023: 4070-4077 - [c96]Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel, Uddalok Sarkar, Sayantan Sen:
Testing of Horn Samplers. AISTATS 2023: 1301-1330 - [c95]Jiong Yang, Kuldeep S. Meel:
Rounding Meets Approximate Model Counting. CAV (2) 2023: 132-162 - [c94]Priyanka Golia, Subhajit Roy, Kuldeep S. Meel:
Synthesis with Explicit Dependencies. DATE 2023: 1-6 - [c93]Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel:
"How Biased are Your Features?": Computing Fairness Influence Functions with Global Sensitivity Analysis. FAccT 2023: 138-148 - [c92]Kuldeep S. Meel:
Distribution Testing: The New Frontier for Formal Methods. FMCAD 2023: 2 - [c91]Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Approximate Model Counting: Is SAT Oracle More Powerful Than NP Oracle? ICALP 2023: 123:1-123:17 - [c90]Anna L. D. Latour, Arunabha Sen, Kuldeep S. Meel:
Solving the Identifying Code Set Problem with Grouped Independent Support. IJCAI 2023: 1971-1978 - [c89]Mate Soos, Divesh Aggarwal, Sourav Chakraborty, Kuldeep S. Meel, Maciej Obremski:
Engineering an Efficient Approximate DNF-Counter. IJCAI 2023: 2031-2038 - [c88]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran:
On Approximating Total Variation Distance. IJCAI 2023: 3479-3487 - [c87]Suwei Yang, Victor C. Liang, Kuldeep S. Meel:
Scalable Probabilistic Routes. ENIGMA@KR 2023: 64-74 - [c86]Mohimenul Kabir, Kuldeep S. Meel:
A Fast and Accurate ASP Counting Based Network Reliability Estimator. LPAR 2023: 270-287 - [c85]Suwei Yang, Victor C. Liang, Kuldeep S. Meel:
Scalable Probabilistic Routes. LPAR 2023: 457-472 - [c84]Diptarka Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Support Size Estimation: The Power of Conditioning. MFCS 2023: 33:1-33:13 - [c83]Timothy van Bremen, Kuldeep S. Meel:
Probabilistic Query Evaluation: The Combined FPRAS Landscape. PODS 2023: 339-347 - [c82]Jiong Yang, Arijit Shaw, Teodora Baluta, Mate Soos, Kuldeep S. Meel:
Explaining SAT Solving Using Causal Reasoning. SAT 2023: 28:1-28:19 - [i66]Sourav Chakraborty, N. V. Vinodchandran, Kuldeep S. Meel:
Distinct Elements in Streams: An Algorithm for the (Text) Book. CoRR abs/2301.10191 (2023) - [i65]Priyanka Golia, Subhajit Roy, Kuldeep S. Meel:
Synthesis with Explicit Dependencies. CoRR abs/2301.10556 (2023) - [i64]Aduri Pavan, Kuldeep S. Meel, N. V. Vinodchandran, Arnab Bhattacharyya:
Constraint Optimization over Semirings. CoRR abs/2302.12937 (2023) - [i63]Jiong Yang, Kuldeep S. Meel:
Rounding Meets Approximate Model Counting. CoRR abs/2305.09247 (2023) - [i62]Jiong Yang, Arijit Shaw, Teodora Baluta, Mate Soos, Kuldeep S. Meel:
Explaining SAT Solving Using Causal Reasoning. CoRR abs/2306.06294 (2023) - [i61]Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Approximate Model Counting: Is SAT Oracle More Powerful than NP Oracle? CoRR abs/2306.10281 (2023) - [i60]Suwei Yang, Victor C. Liang, Kuldeep S. Meel:
Scalable Probabilistic Routes. CoRR abs/2306.10736 (2023) - [i59]Suwei Yang, Victor C. Liang, Kuldeep S. Meel:
INC: A Scalable Incremental Weighted Sampler. CoRR abs/2306.10824 (2023) - [i58]Yash Pote, Kuldeep S. Meel:
On Scalable Testing of Samplers. CoRR abs/2306.13958 (2023) - [i57]Anna L. D. Latour, Arunabha Sen, Kuldeep S. Meel:
Solving the Identifying Code Set Problem with Grouped Independent Support. CoRR abs/2306.15693 (2023) - [i56]Gunjan Kumar, Kuldeep S. Meel, Yash Pote:
Tolerant Testing of High-Dimensional Samplers with Subcube Conditioning. CoRR abs/2308.04264 (2023) - [i55]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran:
Total Variation Distance Estimation Is as Easy as Probabilistic Inference. CoRR abs/2309.09134 (2023) - [i54]Antoine Amarilli, Timothy van Bremen, Kuldeep S. Meel:
Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability. CoRR abs/2309.13287 (2023) - [i53]Yacine Izza, Kuldeep S. Meel, João Marques-Silva:
Locally-Minimal Probabilistic Explanations. CoRR abs/2312.11831 (2023) - [i52]Mohimenul Kabir, Supratik Chakraborty, Kuldeep S. Meel:
Exact ASP Counting with Compact Encodings. CoRR abs/2312.11936 (2023) - [i51]Arijit Shaw, Brendan Juba, Kuldeep S. Meel:
An Approximate Skolem Function Counter. CoRR abs/2312.12026 (2023) - [i50]Suwei Yang, Kuldeep S. Meel:
Engineering an Exact Pseudo-Boolean Model Counter. CoRR abs/2312.12341 (2023) - [i49]Kuldeep S. Meel, Supratik Chakraborty, S. Akshay:
Auditable Algorithms for Approximate Model Counting. CoRR abs/2312.12362 (2023) - [i48]Kuldeep S. Meel, Sourav Chakraborty, Umang Mathur:
A faster FPRAS for #NFA. CoRR abs/2312.13320 (2023) - [i47]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran:
Total Variation Distance Estimation Is as Easy as Probabilistic Inference. Electron. Colloquium Comput. Complex. TR23 (2023) - 2022
- [j5]Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel:
Efficient Learning of Interpretable Classification Rules. J. Artif. Intell. Res. 74: 1823-1863 (2022) - [j4]Aduri Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel:
Model Counting Meets Distinct Elements in a Data Stream. SIGMOD Rec. 51(1): 87-94 (2022) - [c81]Mohimenul Kabir, Flavio O. Everardo, Ankit K. Shukla, Markus Hecher, Johannes Klaus Fichte, Kuldeep S. Meel:
ApproxASP - a Scalable Approximate Answer Set Counter. AAAI 2022: 5755-5764 - [c80]Aditya A. Shrotri, Nina Narodytska, Alexey Ignatiev, Kuldeep S. Meel, João Marques-Silva, Moshe Y. Vardi:
Constraint-Driven Explanations for Black-Box ML Models. AAAI 2022: 8304-8314 - [c79]Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel:
Algorithmic Fairness Verification with Graphical Models. AAAI 2022: 9539-9548 - [c78]Jiong Yang, Supratik Chakraborty, Kuldeep S. Meel:
Projected Model Counting: Beyond Independent Support. ATVA 2022: 171-187 - [c77]Priyanka Golia, Brendan Juba, Kuldeep S. Meel:
A Scalable Shannon Entropy Estimator. CAV (1) 2022: 363-384 - [c76]Mate Soos, Priyanka Golia, Sourav Chakraborty, Kuldeep S. Meel:
On Quantitative Testing of Samplers. CP 2022: 36:1-36:16 - [c75]Sourav Chakraborty, N. V. Vinodchandran, Kuldeep S. Meel:
Distinct Elements in Streams: An Algorithm for the (Text) Book. ESA 2022: 34:1-34:6 - [c74]Suwei Yang, Victor C. Liang, Kuldeep S. Meel:
INC: A Scalable Incremental Weighted Sampler. FMCAD 2022: 205-213 - [c73]Mate Soos, Kuldeep S. Meel:
Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Sampling. ICCAD 2022: 71:1-71:9 - [c72]Eduard Baranov, Sourav Chakraborty, Axel Legay, Kuldeep S. Meel, N. Variyam Vinodchandran:
A Scalable t-wise Coverage Estimator. ICSE 2022: 36-47 - [c71]Kuldeep S. Meel:
Counting, Sampling, and Synthesis: The Quest for Scalability. IJCAI 2022: 5816-5820 - [c70]Remi Delannoy, Kuldeep S. Meel:
On Almost-Uniform Generation of SAT Solutions: The power of 3-wise independent hashing. LICS 2022: 17:1-17:10 - [c69]Yash Pote, Kuldeep S. Meel:
On Scalable Testing of Samplers. NeurIPS 2022 - [c68]Kuldeep S. Meel, Sourav Chakraborty, N. V. Vinodchandran:
Estimation of the Size of Union of Delphic Sets: Achieving Independence from Stream Size. PODS 2022: 41-52 - [e1]Kuldeep S. Meel, Ofer Strichman:
25th International Conference on Theory and Applications of Satisfiability Testing, SAT 2022, August 2-5, 2022, Haifa, Israel. LIPIcs 236, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2022, ISBN 978-3-95977-242-6 [contents] - [i46]Yong Lai, Kuldeep S. Meel, Roland H. C. Yap:
CCDD: A Tractable Representation for Model Counting and Uniform Sampling. CoRR abs/2202.10025 (2022) - [i45]Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel:
Efficient Learning of Interpretable Classification Rules. CoRR abs/2205.06936 (2022) - [i44]Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel:
How Biased is Your Feature?: Computing Fairness Influence Functions with Global Sensitivity Analysis. CoRR abs/2206.00667 (2022) - [i43]Priyanka Golia, Brendan Juba, Kuldeep S. Meel:
A Scalable Shannon Entropy Estimator. CoRR abs/2206.00921 (2022) - [i42]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, Aduri Pavan, N. V. Vinodchandran:
On Approximating Total Variation Distance. CoRR abs/2206.07209 (2022) - [i41]Diptarka Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Support Size Estimation: The Power of Conditioning. CoRR abs/2211.11967 (2022) - [i40]Yong Lai, Kuldeep S. Meel, Roland H. C. Yap:
Fast Converging Anytime Model Counting. CoRR abs/2212.09390 (2022) - 2021
- [c67]Jaroslav Bendík, Kuldeep S. Meel:
Counting Maximal Satisfiable Subsets. AAAI 2021: 3651-3660 - [c66]Yong Lai, Kuldeep S. Meel, Roland H. C. Yap:
The Power of Literal Equivalence in Model Counting. AAAI 2021: 3851-3859 - [c65]Timothy van Bremen, Vincent Derkinderen, Shubham Sharma, Subhajit Roy, Kuldeep S. Meel:
Symmetric Component Caching for Model Counting on Combinatorial Instances. AAAI 2021: 3922-3930 - [c64]Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel:
Justicia: A Stochastic SAT Approach to Formally Verify Fairness. AAAI 2021: 7554-7563 - [c63]Suwei Yang, Massimo Lupascu, Kuldeep S. Meel:
Predicting Forest Fire Using Remote Sensing Data And Machine Learning. AAAI 2021: 14983-14990 - [c62]Jaroslav Bendík, Kuldeep S. Meel:
Counting Minimal Unsatisfiable Subsets. CAV (2) 2021: 313-336 - [c61]Jiong Yang, Kuldeep S. Meel:
Engineering an Efficient PB-XOR Solver. CP 2021: 58:1-58:20 - [c60]Gilles Pesant, Kuldeep S. Meel, Mahshid Mohammadalitajrishi:
On the Usefulness of Linear Modular Arithmetic in Constraint Programming. CPAIOR 2021: 248-265 - [c59]Priyanka Golia, Mate Soos, Sourav Chakraborty, Kuldeep S. Meel:
Designing Samplers is Easy: The Boon of Testers. FMCAD 2021: 222-230 - [c58]Priyanka Golia, Friedrich Slivovsky, Subhajit Roy, Kuldeep S. Meel:
Engineering an Efficient Boolean Functional Synthesis Engine. ICCAD 2021: 1-9 - [c57]Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel, Prateek Saxena:
Scalable Quantitative Verification for Deep Neural Networks. ICSE (Companion Volume) 2021: 248-249 - [c56]Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel, Prateek Saxena:
Scalable Quantitative Verification For Deep Neural Networks. ICSE 2021: 312-323 - [c55]Priyanka Golia, Subhajit Roy, Kuldeep S. Meel:
Program Synthesis as Dependency Quantified Formula Modulo Theory. IJCAI 2021: 1894-1900 - [c54]Durgesh Agrawal, Yash Pote, Kuldeep S. Meel:
Partition Function Estimation: A Quantitative Study. IJCAI 2021: 4276-4285 - [c53]Mate Soos, Kuldeep S. Meel:
Gaussian Elimination Meets Maximum Satisfiability. KR 2021: 581-587 - [c52]Yash Pote, Kuldeep S. Meel:
Testing Probabilistic Circuits. NeurIPS 2021: 22336-22347 - [c51]Kuldeep S. Meel, N. V. Vinodchandran, Sourav Chakraborty:
Estimating the Size of Union of Sets in Streaming Models. PODS 2021: 126-137 - [c50]Aduri Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel:
Model Counting meets F0 Estimation. PODS 2021: 299-311 - [c49]Nicolas Prevot, Mate Soos, Kuldeep S. Meel:
Leveraging GPUs for Effective Clause Sharing in Parallel SAT Solving. SAT 2021: 471-487 - [p1]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
Approximate Model Counting. Handbook of Satisfiability 2021: 1015-1045 - [i39]Suwei Yang, Massimo Lupascu, Kuldeep S. Meel:
Predicting Forest Fire Using Remote Sensing Data And Machine Learning. CoRR abs/2101.01975 (2021) - [i38]Aduri Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel:
Model Counting meets F0 Estimation. CoRR abs/2105.00639 (2021) - [i37]Priyanka Golia, Subhajit Roy, Kuldeep S. Meel:
Program Synthesis as Dependency Quantified Formula Modulo Theory. CoRR abs/2105.09221 (2021) - [i36]Durgesh Agrawal, Yash Pote, Kuldeep S. Meel:
Partition Function Estimation: A Quantitative Study. CoRR abs/2105.11132 (2021) - [i35]Priyanka Golia, Friedrich Slivovsky, Subhajit Roy, Kuldeep S. Meel:
Engineering an Efficient Boolean Functional Synthesis Engine. CoRR abs/2108.05717 (2021) - [i34]Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel:
Algorithmic Fairness Verification with Graphical Models. CoRR abs/2109.09447 (2021) - [i33]Mate Soos, Kuldeep S. Meel:
Arjun: An Efficient Independent Support Computation Technique and its Applications to Counting and Sampling. CoRR abs/2110.09026 (2021) - [i32]Jiong Yang, Supratik Chakraborty, Kuldeep S. Meel:
Projected Model Counting: Beyond Independent Support. CoRR abs/2110.09171 (2021) - [i31]Yash Pote, Kuldeep S. Meel:
Testing Probabilistic Circuits. CoRR abs/2112.04941 (2021) - 2020
- [c48]Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett, Kuldeep S. Meel:
A MaxSAT-Based Framework for Group Testing. AAAI 2020: 10144-10152 - [c47]Jaroslav Bendík, Kuldeep S. Meel:
Approximate Counting of Minimal Unsatisfiable Subsets. CAV (1) 2020: 439-462 - [c46]Mate Soos, Stephan Gocht, Kuldeep S. Meel:
Tinted, Detached, and Lazy CNF-XOR Solving and Its Applications to Counting and Sampling. CAV (1) 2020: 463-484 - [c45]Priyanka Golia, Subhajit Roy, Kuldeep S. Meel:
Manthan: A Data-Driven Approach for Boolean Function Synthesis. CAV (2) 2020: 611-633 - [c44]Rahul Gupta, Subhajit Roy, Kuldeep S. Meel:
Phase Transition Behavior in Knowledge Compilation. CP 2020: 358-374 - [c43]Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel:
Classification Rules in Relaxed Logical Form. ECAI 2020: 2489-2496 - [c42]Kuldeep S. Meel, S. Akshay:
Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice. LICS 2020: 728-741 - [c41]A. Dileep, Kuldeep S. Meel, Ammar Fathin Sabili:
Induction Models on N. LPAR 2020: 169-190 - [c40]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran:
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning. NeurIPS 2020 - [c39]Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel:
Taming Discrete Integration via the Boon of Dimensionality. NeurIPS 2020 - [c38]Kuldeep S. Meel, Yash Pote, Sourav Chakraborty:
On Testing of Samplers. NeurIPS 2020 - [c37]Arijit Shaw, Kuldeep S. Meel:
Designing New Phase Selection Heuristics. SAT 2020: 72-88 - [c36]Durgesh Agrawal, Bhavishya, Kuldeep S. Meel:
On the Sparsity of XORs in Approximate Model Counting. SAT 2020: 250-266 - [c35]Eduard Baranov, Axel Legay, Kuldeep S. Meel:
Baital: an adaptive weighted sampling approach for improved t-wise coverage. ESEC/SIGSOFT FSE 2020: 1114-1126 - [c34]Wenxi Wang, Muhammad Usman, Alyas Almaawi, Kaiyuan Wang, Kuldeep S. Meel, Sarfraz Khurshid:
A Study of Symmetry Breaking Predicates and Model Counting. TACAS (1) 2020: 115-134 - [d1]S. Akshay, Kuldeep S. Meel:
Dataset for Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice. Zenodo, 2020
- [i30]Bishwamittra Ghosh, Kuldeep S. Meel:
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules. CoRR abs/2001.01891 (2020) - [i29]Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran:
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning. CoRR abs/2002.05378 (2020) - [i28]Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel, Prateek Saxena:
Scalable Quantitative Verification For Deep Neural Networks. CoRR abs/2002.06864 (2020) - [i27]Kuldeep S. Meel, S. Akshay:
Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice. CoRR abs/2004.14692 (2020) - [i26]Arijit Shaw, Kuldeep S. Meel:
Designing New Phase Selection Heuristics. CoRR abs/2005.04850 (2020) - [i25]Priyanka Golia, Subhajit Roy, Kuldeep S. Meel:
Manthan: A Data Driven Approach for Boolean Function Synthesis. CoRR abs/2005.06922 (2020) - [i24]Rahul Gupta, Subhajit Roy, Kuldeep S. Meel:
Phase Transition Behavior in Knowledge Compilation. CoRR abs/2007.10400 (2020) - [i23]A. Dileep, Kuldeep S. Meel, Ammar Fathin Sabili:
Induction Models on \mathbb{N}. CoRR abs/2008.06410 (2020) - [i22]Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel:
Justicia: A Stochastic SAT Approach to Formally Verify Fairness. CoRR abs/2009.06516 (2020) - [i21]Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel:
Taming Discrete Integration via the Boon of Dimensionality. CoRR abs/2010.10724 (2020) - [i20]Kuldeep S. Meel, Yash Pote, Sourav Chakraborty:
On Testing of Samplers. CoRR abs/2010.12918 (2020)
2010 – 2019
- 2019
- [j3]Kuldeep S. Meel, Aditya A. Shrotri, Moshe Y. Vardi:
Not all FPRASs are equal: demystifying FPRASs for DNF-counting. Constraints An Int. J. 24(3-4): 211-233 (2019) - [j2]Roger Paredes, Leonardo Dueñas-Osorio, Kuldeep S. Meel, Moshe Y. Vardi:
Principled network reliability approximation: A counting-based approach. Reliab. Eng. Syst. Saf. 191 (2019) - [c33]Mate Soos, Kuldeep S. Meel:
BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting. AAAI 2019: 1592-1599 - [c32]Sourav Chakraborty, Kuldeep S. Meel:
On Testing of Uniform Samplers. AAAI 2019: 7777-7784 - [c31]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
On the Hardness of Probabilistic Inference Relaxations. AAAI 2019: 7785-7792 - [c30]Bishwamittra Ghosh, Kuldeep S. Meel:
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules. AIES 2019: 203-210 - [c29]Teodora Baluta, Shiqi Shen, Shweta Shinde, Kuldeep S. Meel, Prateek Saxena:
Quantitative Verification of Neural Networks and Its Security Applications. CCS 2019: 1249-1264 - [c28]Alexis de Colnet, Kuldeep S. Meel:
Dual Hashing-Based Algorithms for Discrete Integration. CP 2019: 161-176 - [c27]Davin Choo, Mate Soos, Kian Ming Adam Chai, Kuldeep S. Meel:
Bosphorus: Bridging ANF and CNF Solvers. DATE 2019: 468-473 - [c26]Yash Pote, Saurabh Joshi, Kuldeep S. Meel:
Phase Transition Behavior of Cardinality and XOR Constraints. IJCAI 2019: 1162-1168 - [c25]Shubham Sharma, Subhajit Roy, Mate Soos, Kuldeep S. Meel:
GANAK: A Scalable Probabilistic Exact Model Counter. IJCAI 2019: 1169-1176 - [c24]Kuldeep S. Meel, Aditya A. Shrotri, Moshe Y. Vardi:
Not All FPRASs are Equal: Demystifying FPRASs for DNF-Counting (Extended Abstract). IJCAI 2019: 6211-6215 - [c23]Yaqi Xie, Ziwei Xu, Kuldeep S. Meel, Mohan S. Kankanhalli, Harold Soh:
Embedding Symbolic Knowledge into Deep Networks. NeurIPS 2019: 4235-4245 - [c22]Nina Narodytska, Aditya A. Shrotri, Kuldeep S. Meel, Alexey Ignatiev, João Marques-Silva:
Assessing Heuristic Machine Learning Explanations with Model Counting. SAT 2019: 267-278 - [c21]Mate Soos, Raghav Kulkarni, Kuldeep S. Meel:
CrystalBall: Gazing in the Black Box of SAT Solving. SAT 2019: 371-387 - [c20]Rahul Gupta, Shubham Sharma, Subhajit Roy, Kuldeep S. Meel:
WAPS: Weighted and Projected Sampling. TACAS (1) 2019: 59-76 - [i19]Teodora Baluta, Shiqi Shen, Shweta Shinde, Kuldeep S. Meel, Prateek Saxena:
Quantitative Verification of Neural Networks And its Security Applications. CoRR abs/1906.10395 (2019) - [i18]Yaqi Xie, Ziwei Xu, Kuldeep S. Meel, Mohan S. Kankanhalli, Harold Soh:
Semantically-Regularized Logic Graph Embeddings. CoRR abs/1909.01161 (2019) - [i17]Yash Pote, Saurabh Joshi, Kuldeep S. Meel:
Phase Transition Behavior of Cardinality and XOR Constraints. CoRR abs/1910.09755 (2019) - 2018
- [c19]Dmitry Malioutov, Kuldeep S. Meel:
MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules. CP 2018: 312-327 - [c18]Shubham Sharma, Rahul Gupta, Subhajit Roy, Kuldeep S. Meel:
Knowledge Compilation meets Uniform Sampling. LPAR 2018: 620-636 - [c17]Fabrizio Biondi, Michael A. Enescu, Annelie Heuser, Axel Legay, Kuldeep S. Meel, Jean Quilbeuf:
Scalable Approximation of Quantitative Information Flow in Programs. VMCAI 2018: 71-93 - [i16]Roger Paredes, Leonardo Dueñas-Osorio, Kuldeep S. Meel, Moshe Y. Vardi:
Network Reliability Estimation in Theory and Practice. CoRR abs/1806.00917 (2018) - [i15]Kuldeep S. Meel:
Constrained Counting and Sampling: Bridging the Gap between Theory and Practice. CoRR abs/1806.02239 (2018) - [i14]Dmitry Malioutov, Kuldeep S. Meel:
MLIC: A MaxSAT-Based framework for learning interpretable classification rules. CoRR abs/1812.01843 (2018) - [i13]Davin Choo, Mate Soos, Kian Ming Adam Chai, Kuldeep S. Meel:
BOSPHORUS: Bridging ANF and CNF Solvers. CoRR abs/1812.04580 (2018) - 2017
- [c16]Leonardo Dueñas-Osorio, Kuldeep S. Meel, Roger Paredes, Moshe Y. Vardi:
Counting-Based Reliability Estimation for Power-Transmission Grids. AAAI 2017: 4488-4494 - [c15]Kuldeep S. Meel, Aditya A. Shrotri, Moshe Y. Vardi:
On Hashing-Based Approaches to Approximate DNF-Counting. FSTTCS 2017: 41:1-41:14 - [c14]Jeffrey M. Dudek, Kuldeep S. Meel, Moshe Y. Vardi:
The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas. IJCAI 2017: 600-606 - [i12]Jeffrey M. Dudek, Kuldeep S. Meel, Moshe Y. Vardi:
Combining the k-CNF and XOR Phase-Transitions. CoRR abs/1702.08392 (2017) - [i11]Kuldeep S. Meel, Aditya A. Shrotri, Moshe Y. Vardi:
On Hashing-Based Approaches to Approximate DNF-Counting. CoRR abs/1710.05247 (2017) - [i10]Jeffrey M. Dudek, Kuldeep S. Meel, Moshe Y. Vardi:
The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas. CoRR abs/1710.06378 (2017) - 2016
- [j1]Alexander Ivrii, Sharad Malik, Kuldeep S. Meel, Moshe Y. Vardi:
On computing minimal independent support and its applications to sampling and counting. Constraints An Int. J. 21(1): 41-58 (2016) - [c13]Supratik Chakraborty, Kuldeep S. Meel, Rakesh Mistry, Moshe Y. Vardi:
Approximate Probabilistic Inference via Word-Level Counting. AAAI 2016: 3218-3224 - [c12]Kuldeep S. Meel, Moshe Y. Vardi, Supratik Chakraborty, Daniel J. Fremont, Sanjit A. Seshia, Dror Fried, Alexander Ivrii, Sharad Malik:
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving. AAAI Workshop: Beyond NP 2016 - [c11]Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar:
Automatic data layout generation and kernel mapping for CPU+GPU architectures. CC 2016: 240-250 - [c10]Karthik Murthy, Sri Raj Paul, Kuldeep S. Meel, Tiago Cogumbreiro, John M. Mellor-Crummey:
Design and Verification of Distributed Phasers. Euro-Par 2016: 405-418 - [c9]Jeffrey M. Dudek, Kuldeep S. Meel, Moshe Y. Vardi:
Combining the k-CNF and XOR Phase-Transitions. IJCAI 2016: 727-734 - [c8]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
Algorithmic Improvements in Approximate Counting for Probabilistic Inference: From Linear to Logarithmic SAT Calls. IJCAI 2016: 3569-3576 - 2015
- [c7]Supratik Chakraborty, Dror Fried, Kuldeep S. Meel, Moshe Y. Vardi:
From Weighted to Unweighted Model Counting. IJCAI 2015: 689-695 - [c6]Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi:
On Parallel Scalable Uniform SAT Witness Generation. TACAS 2015: 304-319 - [i9]Supratik Chakraborty, Kuldeep S. Meel, Rakesh Mistry, Moshe Y. Vardi:
Approximate Probabilistic Inference via Word-Level Counting. CoRR abs/1511.07663 (2015) - [i8]Kuldeep S. Meel, Moshe Y. Vardi, Supratik Chakraborty, Daniel J. Fremont, Sanjit A. Seshia, Dror Fried, Alexander Ivrii, Sharad Malik:
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving. CoRR abs/1512.06633 (2015) - [i7]Sri Raj Paul, Karthik Murthy, Kuldeep S. Meel, John M. Mellor-Crummey:
Distributed Phasers. CoRR abs/1512.07305 (2015) - 2014
- [c5]Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar:
ADHA: automatic data layout framework for heterogeneous architectures. PACT 2014: 479-480 - [c4]Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi:
Distribution-Aware Sampling and Weighted Model Counting for SAT. AAAI 2014: 1722-1730 - [c3]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
Balancing Scalability and Uniformity in SAT Witness Generator. DAC 2014: 60:1-60:6 - [i6]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
Balancing Scalability and Uniformity in SAT Witness Generator. CoRR abs/1403.6246 (2014) - [i5]Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi:
Distribution-Aware Sampling and Weighted Model Counting for SAT. CoRR abs/1404.2984 (2014) - [i4]Kuldeep S. Meel:
Sampling Techniques for Boolean Satisfiability. CoRR abs/1404.6682 (2014) - [i3]Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar:
ADHA: Automatic Data layout framework for Heterogeneous Architectures. CoRR abs/1407.4859 (2014) - 2013
- [c2]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
A Scalable and Nearly Uniform Generator of SAT Witnesses. CAV 2013: 608-623 - [c1]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
A Scalable Approximate Model Counter. CP 2013: 200-216 - [i2]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
A Scalable and Nearly Uniform Generator of SAT Witnesses. CoRR abs/1304.1584 (2013) - [i1]Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi:
A Scalable Approximate Model Counter. CoRR abs/1306.5726 (2013)
Coauthor Index
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