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  • Stankovič M and Bartocci E. Probabilistic Loop Synthesis from Sequences of Moments. Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems. (233-248).

    https://doi.org/10.1007/978-3-031-68416-6_14

  • Sarkar A. (2024). Automated quantum software engineering. Automated Software Engineering. 31:1. Online publication date: 1-May-2024.

    https://doi.org/10.1007/s10515-024-00436-x

  • Mell S, Bastani F, Zdancewic S and Bastani O. Synthesizing Trajectory Queries from Examples. Computer Aided Verification. (459-484).

    https://doi.org/10.1007/978-3-031-37706-8_23

  • Klaus J, Blacher M, Goral A, Lucas P and Giesen J. (2023). A visual analytics workflow for probabilistic modeling. Visual Informatics. 10.1016/j.visinf.2023.05.001. 7:2. (72-84). Online publication date: 1-Jun-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S2468502X23000153

  • Andriushchenko R, Češka M, Junges S, Katoen J and Stupinský Š. (2021). PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs. Computer Aided Verification. 10.1007/978-3-030-81685-8_40. (856-869).

    https://link.springer.com/10.1007/978-3-030-81685-8_40

  • Andriushchenko R, Češka M, Junges S and Katoen J. (2021). Inductive Synthesis for Probabilistic Programs Reaches New Horizons. Tools and Algorithms for the Construction and Analysis of Systems. 10.1007/978-3-030-72016-2_11. (191-209).

    http://link.springer.com/10.1007/978-3-030-72016-2_11

  • Laurel J and Misailovic S. (2020). Continualization of Probabilistic Programs With Correction. Programming Languages and Systems. 10.1007/978-3-030-44914-8_14. (366-393).

    http://link.springer.com/10.1007/978-3-030-44914-8_14

  • Izycheva A, Darulova E and Seidl H. Synthesizing Efficient Low-Precision Kernels. Automated Technology for Verification and Analysis. (294-313).

    https://doi.org/10.1007/978-3-030-31784-3_17

  • Sherman B, Michel J and Carbin M. (2019). Sound and robust solid modeling via exact real arithmetic and continuity. Proceedings of the ACM on Programming Languages. 3:ICFP. (1-29). Online publication date: 26-Jul-2019.

    https://doi.org/10.1145/3341703

  • Paraskevopoulou Z and Appel A. (2019). Closure conversion is safe for space. Proceedings of the ACM on Programming Languages. 3:ICFP. (1-29). Online publication date: 26-Jul-2019.

    https://doi.org/10.1145/3341687

  • Delaware B, Suriyakarn S, Pit-Claudel C, Ye Q and Chlipala A. (2019). Narcissus: correct-by-construction derivation of decoders and encoders from binary formats. Proceedings of the ACM on Programming Languages. 3:ICFP. (1-29). Online publication date: 26-Jul-2019.

    https://doi.org/10.1145/3341686

  • Cong Y, Osvald L, Essertel G and Rompf T. (2019). Compiling with continuations, or without? whatever.. Proceedings of the ACM on Programming Languages. 3:ICFP. (1-28). Online publication date: 26-Jul-2019.

    https://doi.org/10.1145/3341643

  • Flatt M, Derici C, Dybvig R, Keep A, Massaccesi G, Spall S, Tobin-Hochstadt S and Zeppieri J. (2019). Rebuilding racket on chez scheme (experience report). Proceedings of the ACM on Programming Languages. 3:ICFP. (1-15). Online publication date: 26-Jul-2019.

    https://doi.org/10.1145/3341642

  • Vákár M, Kammar O and Staton S. (2019). A domain theory for statistical probabilistic programming. Proceedings of the ACM on Programming Languages. 3:POPL. (1-29). Online publication date: 2-Jan-2019.

    https://doi.org/10.1145/3290349

  • Gorinova M, Gordon A and Sutton C. (2019). Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministic. Proceedings of the ACM on Programming Languages. 3:POPL. (1-30). Online publication date: 2-Jan-2019.

    https://doi.org/10.1145/3290348

  • Unruh D. (2019). Quantum relational Hoare logic. Proceedings of the ACM on Programming Languages. 3:POPL. (1-31). Online publication date: 2-Jan-2019.

    https://doi.org/10.1145/3290346

  • Češka M, Dehnert C, Jansen N, Junges S and Katoen J. (2019). Model Repair Revamped. From Reactive Systems to Cyber-Physical Systems. 10.1007/978-3-030-31514-6_7. (107-125).

    http://link.springer.com/10.1007/978-3-030-31514-6_7

  • Češka M, Hensel C, Junges S and Katoen J. (2019). Counterexample-Driven Synthesis for Probabilistic Program Sketches. Formal Methods – The Next 30 Years. 10.1007/978-3-030-30942-8_8. (101-120).

    http://link.springer.com/10.1007/978-3-030-30942-8_8

  • Drews S, Albarghouthi A and D’Antoni L. (2019). Efficient Synthesis with Probabilistic Constraints. Computer Aided Verification. 10.1007/978-3-030-25540-4_15. (278-296).

    http://link.springer.com/10.1007/978-3-030-25540-4_15

  • Češka M, Jansen N, Junges S and Katoen J. (2019). Shepherding Hordes of Markov Chains. Advances in Knowledge Discovery and Data Mining. 10.1007/978-3-030-17465-1_10. (172-190).

    http://link.springer.com/10.1007/978-3-030-17465-1_10

  • Cusumano-Towner M, Bichsel B, Gehr T, Vechev M and Mansinghka V. (2018). Incremental inference for probabilistic programs. ACM SIGPLAN Notices. 53:4. (571-585). Online publication date: 2-Dec-2018.

    https://doi.org/10.1145/3296979.3192399

  • Cusumano-Towner M, Bichsel B, Gehr T, Vechev M and Mansinghka V. Incremental inference for probabilistic programs. Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation. (571-585).

    https://doi.org/10.1145/3192366.3192399

  • Nandi C, Grossman D, Sampson A, Mytkowicz T and McKinley K. Debugging probabilistic programs. Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages. (18-26).

    https://doi.org/10.1145/3088525.3088564

  • Cardelli L, Češka M, Fränzle M, Kwiatkowska M, Laurenti L, Paoletti N and Whitby M. (2017). Syntax-Guided Optimal Synthesis for Chemical Reaction Networks. Computer Aided Verification. 10.1007/978-3-319-63390-9_20. (375-395).

    http://link.springer.com/10.1007/978-3-319-63390-9_20

  • Huot M, Ghavami M, Lew A, Schaechtle U, Freer C, Shelby Z, Rinard M, Saad F and Mansinghka V. (2024). GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. Proceedings of the ACM on Programming Languages. 8:PLDI. (790-815). Online publication date: 20-Jun-2024.

    https://doi.org/10.1145/3656409

  • D’Antoni L, Hu Q, Kim J and Reps T. (2021). Programmable Program Synthesis. Computer Aided Verification. 10.1007/978-3-030-81685-8_4. (84-109).

    https://link.springer.com/10.1007/978-3-030-81685-8_4

  • Laurel J and Misailovic S. Continualization of Probabilistic Programs With Correction. Programming Languages and Systems. (366-393).

    https://doi.org/10.1007/978-3-030-44914-8_14

  • Saad F, Cusumano-Towner M, Schaechtle U, Rinard M and Mansinghka V. (2019). Bayesian synthesis of probabilistic programs for automatic data modeling. Proceedings of the ACM on Programming Languages. 3:POPL. (1-32). Online publication date: 2-Jan-2019.

    https://doi.org/10.1145/3290350

  • Gehr T, Misailovic S, Tsankov P, Vanbever L, Wiesmann P and Vechev M. (2018). Bayonet: probabilistic inference for networks. ACM SIGPLAN Notices. 53:4. (586-602). Online publication date: 2-Dec-2018.

    https://doi.org/10.1145/3296979.3192400

  • Gehr T, Misailovic S, Tsankov P, Vanbever L, Wiesmann P and Vechev M. Bayonet: probabilistic inference for networks. Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation. (586-602).

    https://doi.org/10.1145/3192366.3192400

  • Chasins S and Phothilimthana P. (2017). Data-Driven Synthesis of Full Probabilistic Programs. Computer Aided Verification. 10.1007/978-3-319-63387-9_14. (279-304).

    https://link.springer.com/10.1007/978-3-319-63387-9_14

  • Heule S, Schkufza E, Sharma R and Aiken A. (2016). Stratified synthesis: automatically learning the x86-64 instruction set. ACM SIGPLAN Notices. 51:6. (237-250). Online publication date: 1-Aug-2016.

    https://doi.org/10.1145/2980983.2908121

  • Katoen J. The Probabilistic Model Checking Landscape. Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science. (31-45).

    https://doi.org/10.1145/2933575.2934574

  • Heule S, Schkufza E, Sharma R and Aiken A. Stratified synthesis: automatically learning the x86-64 instruction set. Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation. (237-250).

    https://doi.org/10.1145/2908080.2908121

  • Raychev V, Bielik P, Vechev M and Krause A. (2016). Learning programs from noisy data. ACM SIGPLAN Notices. 51:1. (761-774). Online publication date: 8-Apr-2016.

    https://doi.org/10.1145/2914770.2837671

  • Singh R and Gulwani S. (2016). Transforming spreadsheet data types using examples. ACM SIGPLAN Notices. 51:1. (343-356). Online publication date: 8-Apr-2016.

    https://doi.org/10.1145/2914770.2837668

  • Raychev V, Bielik P, Vechev M and Krause A. Learning programs from noisy data. Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. (761-774).

    https://doi.org/10.1145/2837614.2837671

  • Singh R and Gulwani S. Transforming spreadsheet data types using examples. Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. (343-356).

    https://doi.org/10.1145/2837614.2837668