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\n  \n 2023\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Robust Metric Hybrid Planning in Stochastic Nonlinear Domains Using Mathematical Optimization.\n \n \n \n \n\n\n \n Say, B.\n\n\n \n\n\n\n In Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling (ICAPS-2023), pages 375–383, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"Robust paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 39 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2023,\n    author = {Say, Buser},\n    title = {Robust Metric Hybrid Planning in Stochastic Nonlinear Domains Using Mathematical Optimization},\n    booktitle = {Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling {(ICAPS-2023)}},\n    year = {2023},\n    pages = {375--383},\n    url_paper = {https://ojs.aaai.org/index.php/ICAPS/article/view/27216}\n}\n\n
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\n \n\n \n \n \n \n \n \n Rapid Identification of Protein Formulations with Bayesian Optimisation.\n \n \n \n \n\n\n \n Huynh, V.; Say, B.; Vogel, P.; Cao, L.; Webb, G. I; and Aleti, A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Second IEEE International Conference on Machine Learning and Applications (ICMLA-2023), pages 776–781, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"Rapid paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Huynh2023,\n    author = {Huynh, Viet and Say, Buser and Vogel, Peter and Cao, Lucy and Webb, Geoffrey I and Aleti, Aldeida},\n    title = {Rapid Identification of Protein Formulations with Bayesian Optimisation},\n    booktitle = {Proceedings of the Twenty-Second IEEE International Conference on Machine Learning and Applications {(ICMLA-2023)}},\n    year = {2023},\n    pages = {776--781},\n    url_paper = {https://ieeexplore.ieee.org/document/10460029}\n}\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming.\n \n \n \n \n\n\n \n Tule, S.; Le, N. H. L.; and Say, B.\n\n\n \n\n\n\n In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI-2022), pages 838–845, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Training paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Tule2022,\n    author = {Tule, Sanjana and Le, Nhi Ha Lan and Say, Buser},\n    title = {Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming},\n    booktitle = {Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence {(SSCI-2022)}},\n    year = {2022},\n    pages = {838--845},\n    url_paper = {https://ieeexplore.ieee.org/abstract/document/10022152}\n}\n\n
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\n  \n 2021\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n A Unified Framework for Planning with Learned Neural Network Transition Models.\n \n \n \n \n\n\n \n Say, B.\n\n\n \n\n\n\n In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-2021), pages 5016–5024, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 61 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2021a,\n    author = {Say, Buser},\n    title = {A Unified Framework for Planning with Learned Neural Network Transition Models},\n    booktitle = {Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence {(AAAI-2021)}},\n    year = {2021},\n    pages = {5016--5024},\n    url_paper = {https://ojs.aaai.org/index.php/AAAI/article/view/16635}\n}\n\n
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\n \n\n \n \n \n \n \n \n Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021.\n \n \n \n \n\n\n \n Say, B.; Sanner, S.; Devriendt, J.; Nordström, J.; and Stuckey, P.\n\n\n \n\n\n\n In Bacchus, F.; Berg, J.; Järvisalo, M.; and Martins, R., editor(s), MaxSAT Evaluation 2021: Solver and Benchmark Descriptions, of Department of Computer Science Report Series B, pages 32–36. Department of Computer Science, University of Helsinki, 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Planning paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{Say2021b,\n    author = {Say, Buser and Sanner, Scott and Devriendt, Jo and Nordstr{\\"{o}}m, Jakob and Stuckey, Peter},\n    title = {Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021},\n    booktitle = {MaxSAT Evaluation 2021: Solver and Benchmark Descriptions},\n    editor = {Bacchus, Fahiem and Berg, Jeremias and J{\\"{a}}rvisalo, Matti and Martins, Ruben},\n    year = {2021},\n    pages = {32--36},\n    publisher = {Department of Computer Science, University of Helsinki},\n    series = {Department of Computer Science Report Series B},\n    url_paper = {https://tuhat.helsinki.fi/ws/files/167851514/mse21proc.pdf#page=32}\n}\n\n
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\n  \n 2020\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Compact and Efficient Encodings for Planning in Factored State and Action Spaces with learned Binarized Neural Network Transition Models.\n \n \n \n \n\n\n \n Say, B.; and Sanner, S.\n\n\n \n\n\n\n Artificial Intelligence (AIJ), 285: 103291. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Compact paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 38 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{Say2020a,\n    author = {Say, Buser and Sanner, Scott},\n    title = {Compact and Efficient Encodings for Planning in Factored State and Action Spaces with learned Binarized Neural Network Transition Models},\n    journal = {Artificial Intelligence {(AIJ)}},\n    year = {2020},\n    volume = {285},\n    pages = {103291},\n    url_paper = {https://www.sciencedirect.com/science/article/abs/pii/S0004370220300503?via%3Dihub}\n}\n\n
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\n \n\n \n \n \n \n \n \n Scalable Planning with Deep Neural Network Learned Transition Models.\n \n \n \n \n\n\n \n Wu, G.; Say, B.; and Sanner, S.\n\n\n \n\n\n\n Journal of Artificial Intelligence Research (JAIR), 68: 571–606. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Scalable paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 53 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{Wu2020,\n    author = {Wu, Ga and Say, Buser and Sanner, Scott},\n    title = {Scalable Planning with Deep Neural Network Learned Transition Models},\n    journal = {Journal of Artificial Intelligence Research {(JAIR)}},\n    year = {2020},\n    volume = {68},\n    pages = {571--606},\n    url_paper = {https://www.jair.org/index.php/jair/article/view/11829}\n}\n\n
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\n \n\n \n \n \n \n \n \n Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models.\n \n \n \n \n\n\n \n Say, B.; Devriendt, J.; Nordström, J.; and Stuckey, P.\n\n\n \n\n\n\n In Proceedings of the Twenty-Sixth International Conference on Principles and Practice of Constraint Programming (CP-2020), pages 917–934, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Theoretical paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 19 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2020b,\n    author    = {Say, Buser and Devriendt, Jo and Nordstr{\\"{o}}m, Jakob and Stuckey, Peter},\n    title     = {Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models},\n    booktitle = {Proceedings of the Twenty-Sixth International Conference on Principles and Practice of Constraint Programming {(CP-2020)}},\n    year      = {2020},\n    pages     = {917--934},\n    url_paper = {https://link.springer.com/chapter/10.1007/978-3-030-58475-7_53}\n}\n\n
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\n \n\n \n \n \n \n \n \n Optimal Planning with Learned Neural Network Transition Models.\n \n \n \n \n\n\n \n Say, B.\n\n\n \n\n\n\n Ph.D. Thesis, University of Toronto, Toronto, ON, Canada, 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Optimal paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{Say2020c,\n    author = {Say, Buser},\n    title = {Optimal Planning with Learned Neural Network Transition Models},\n    year = {2020},\n    school = {University of Toronto},\n    address = {Toronto, ON, Canada},\n    url_paper = {https://tspace.library.utoronto.ca/bitstream/1807/101074/3/Say_Buser_202006_PhD_thesis.pdf}\n}\n\n
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\n  \n 2019\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Reward Potentials for Planning with Learned Neural Network Transition Models.\n \n \n \n \n\n\n \n Say, B.; Sanner, S.; and Thiébaux, S.\n\n\n \n\n\n\n In Proceedings of the Twenty-Fifth International Conference on Principles and Practice of Constraint Programming (CP-2019), pages 674–689, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Reward paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2019a,\n    author = {Say, Buser and Sanner, Scott and Thi{\\'e}baux, Sylvie},\n    title = {Reward Potentials for Planning with Learned Neural Network Transition Models},\n    booktitle = {Proceedings of the Twenty-Fifth International Conference on Principles and Practice of Constraint Programming {(CP-2019)}},\n    year = {2019},\n    pages = {674--689},\n    url_paper = {https://link.springer.com/chapter/10.1007/978-3-030-30048-7_39}\n}\n\n
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\n \n\n \n \n \n \n \n \n Metric Hybrid Factored Planning in Nonlinear Domains with Constraint Generation.\n \n \n \n \n\n\n \n Say, B.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the Sixteenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR-2019), pages 502–518, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Metric paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 15 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2019b,\n    author = {Say, Buser and Sanner, Scott},\n    title = {Metric Hybrid Factored Planning in Nonlinear Domains with Constraint Generation},\n    booktitle = {Proceedings of the Sixteenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research {(CPAIOR-2019)}},\n    year = {2019},\n    pages = {502--518},\n    url_paper = {https://link.springer.com/chapter/10.1007/978-3-030-19212-9_33}\n}\n\n
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\n  \n 2018\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models.\n \n \n \n \n\n\n \n Say, B.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-2018), pages 4815–4821, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Planning paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 11 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2018a,\n    author = {Say, Buser and Sanner, Scott},\n    title = {Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models},\n    booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence {(IJCAI-2018)}},\n    year = {2018},\n    pages = {4815--4821},\n    url_paper = {https://www.ijcai.org/Proceedings/2018/0669.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Symbolic Bucket Elimination for Piecewise Continuous Constrained Optimization.\n \n \n \n \n\n\n \n Ye, Z.; Say, B.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the Fifteenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR-2018), pages 585–594, 2018. \n Recipient of the Best Student Paper Award.\n\n\n\n
\n\n\n\n \n \n \"Symbolic paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 18 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Ye2018,\n    author = {Ye, Zhijiang and Say, Buser and Sanner, Scott},\n    title = {Symbolic Bucket Elimination for Piecewise Continuous Constrained Optimization},\n    booktitle = {Proceedings of the Fifteenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research {(CPAIOR-2018)}},\n    year = {2018},\n    pages = {585--594},\n    url_paper = {https://link.springer.com/chapter/10.1007/978-3-319-93031-2_42},\n    note = {<b>Recipient of the Best Student Paper Award.</b>}\n}\n\n
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\n \n\n \n \n \n \n \n \n Metric Nonlinear Hybrid Planning with Constraint Generation.\n \n \n \n \n\n\n \n Say, B.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the Third Workshop on Planning, Search and Optimization (PlanSOpt-2018), pages 19–25, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Metric paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2018b,\n    author = {Say, Buser and Sanner, Scott},\n    title = {Metric Nonlinear Hybrid Planning with Constraint Generation},\n    booktitle = {Proceedings of the Third Workshop on Planning, Search and Optimization {(PlanSOpt-2018)}},\n    year = {2018},\n    pages = {19--25},\n    url_paper = {http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop06/docs/proceedings.pdf#page=23}\n}\n\n
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\n \n\n \n \n \n \n \n \n Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-integer Linear Programming.\n \n \n \n \n\n\n \n Say, B.; Wu, G.; Zhou, Y. Q.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-2017), pages 750–756, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Nonlinear paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 16 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Say2017a,\n    author = {Say, Buser and Wu, Ga and Zhou, Yu Qing and Sanner, Scott},\n    title = {Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-integer Linear Programming},\n    booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence {(IJCAI-2017)}},\n    year = {2017},\n    pages = {750--756},\n    url_paper = {https://www.ijcai.org/proceedings/2017/0104.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Scalable Planning with Tensorflow for Hybrid Nonlinear Domains.\n \n \n \n \n\n\n \n Wu, G.; Say, B.; and Sanner, S.\n\n\n \n\n\n\n In Proceedings of the Thirty-First Annual Conference on Advances in Neural Information Processing Systems (NIPS-2017), pages 6273–6283, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Scalable paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 31 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{Wu2017,\n    author = {Wu, Ga and Say, Buser and Sanner, Scott},\n    title = {Scalable Planning with Tensorflow for Hybrid Nonlinear Domains},\n    booktitle = {Proceedings of the Thirty-First Annual Conference on Advances in Neural Information Processing Systems {(NIPS-2017)}},\n    year = {2017},\n    pages = {6273--6283},\n    url_paper = {https://papers.nips.cc/paper/7207-scalable-planning-with-tensorflow-for-hybrid-nonlinear-domains.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Mixed-Integer Linear Programming Models for Least-Commitment Partial-Order Planning.\n \n \n \n \n\n\n \n Say, B.\n\n\n \n\n\n\n Master's thesis, University of Toronto, Toronto, ON, Canada, 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Mixed-Integer paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@mastersthesis{Say2017b,\n    author = {Say, Buser},\n    title = {Mixed-Integer Linear Programming Models for Least-Commitment Partial-Order Planning},\n    year = {2017},\n    school = {University of Toronto},\n    address = {Toronto, ON, Canada},\n    url_paper = {https://tspace.library.utoronto.ca/bitstream/1807/76666/3/Say_Buser_201703_MAS_thesis.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility.\n \n \n \n \n\n\n \n Say, B.; Cire, A. A.; and Beck, J. C.\n\n\n \n\n\n\n In Proceedings of the Twenty-Second European Conference on Artificial Intelligence (ECAI-2016), pages 1044–1052, 2016. \n \n\n\n\n
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@inproceedings{Say2016,\n    author = {Say, Buser and Cire, Andre Augusto and Beck, J. Christopher},\n    title = {Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility},\n    booktitle = {Proceedings of the Twenty-Second European Conference on Artificial Intelligence {(ECAI-2016)}},\n    year = {2016},\n    pages = {1044--1052},\n    url_paper = {https://tspace.library.utoronto.ca/bitstream/1807/79352/1/Mathematical%20Programming%20Models_Tspace.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Deriving Pandemic Disease Mitigation Strategies by Mining Social Contact Networks.\n \n \n \n \n\n\n \n Ventresca, M.; Szatan, A.; Say, B.; and Aleman, D.\n\n\n \n\n\n\n In Optimization, Control, and Applications in the Information Age, of Springer Proceedings in Mathematics & Statistics, pages 359–381. Springer International Publishing, 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Deriving paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{Ventresca2015,\n    author = {Ventresca, Mario and Szatan, Alexandra and Say, Buser and Aleman, Dionne},\n    title = {Deriving Pandemic Disease Mitigation Strategies by Mining Social Contact Networks},\n    booktitle = {Optimization, Control, and Applications in the Information Age},\n    year = {2015},\n    pages = {359--381},\n    publisher = {Springer International Publishing},\n    series = {Springer Proceedings in Mathematics \\& Statistics},\n    url_paper = {https://link.springer.com/chapter/10.1007/978-3-319-18567-5_19}\n}\n\n
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\n  \n 2013\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Deriving Pandemic Public Policies from Contact Networks.\n \n \n \n\n\n \n Ventresca, M.; Szatan, A.; Say, B.; and Aleman, D.\n\n\n \n\n\n\n In Proceedings of the Eighth INFORMS Workshop on Data Mining and Health Informatics (INFORMS DM-HI-2013), 2013. \n \n\n\n\n
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@inproceedings{Ventresca2013,\n    author = {Ventresca, Mario and Szatan, Alexandra and Say, Buser and Aleman, Dionne},\n    title = {Deriving Pandemic Public Policies from Contact Networks},\n    booktitle = {Proceedings of the Eighth INFORMS Workshop on Data Mining and Health Informatics {(INFORMS DM-HI-2013)}},\n    year = {2013}\n}\n\n
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