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Buser Say
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2020 – today
- 2024
- [i7]Ari Gestetner, Buser Say:
A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models. CoRR abs/2409.11631 (2024) - 2023
- [c12]Buser Say:
Robust Metric Hybrid Planning in Stochastic Nonlinear Domains Using Mathematical Optimization. ICAPS 2023: 375-383 - [c11]Viet Huynh, Buser Say, Peter Vogel, Lucy Cao, Geoffrey I. Webb, Aldeida Aleti:
Rapid Identification of Protein Formulations with Bayesian Optimisation. ICMLA 2023: 776-781 - 2022
- [c10]Sanjana Tule, Nhi Ha Lan Le, Buser Say:
Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming. SSCI 2022: 838-845 - 2021
- [c9]Buser Say:
A Unified Framework for Planning with Learned Neural Network Transition Models. AAAI 2021: 5016-5024 - [i6]Buser Say, Scott Sanner, Jo Devriendt, Jakob Nordström, Peter J. Stuckey:
Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021. CoRR abs/2108.00633 (2021) - [i5]Sanjana Tule, Nhi Ha Lan Le, Buser Say:
Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming. CoRR abs/2112.00434 (2021) - 2020
- [j2]Buser Say, Scott Sanner:
Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition models. Artif. Intell. 285: 103291 (2020) - [j1]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Deep Neural Network Learned Transition Models. J. Artif. Intell. Res. 68: 571-606 (2020) - [c8]Buser Say, Jo Devriendt, Jakob Nordström, Peter J. Stuckey:
Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models. CP 2020: 917-934
2010 – 2019
- 2019
- [c7]Buser Say, Scott Sanner, Sylvie Thiébaux:
Reward Potentials for Planning with Learned Neural Network Transition Models. CP 2019: 674-689 - [c6]Buser Say, Scott Sanner:
Metric Hybrid Factored Planning in Nonlinear Domains with Constraint Generation. CPAIOR 2019: 502-518 - [i4]Ga Wu, Buser Say, Scott Sanner:
Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models. CoRR abs/1904.02873 (2019) - [i3]Buser Say, Scott Sanner, Sylvie Thiébaux:
Reward Potentials for Planning with Learned Neural Network Transition Models. CoRR abs/1904.09366 (2019) - 2018
- [c5]Zhijiang Ye, Buser Say, Scott Sanner:
Symbolic Bucket Elimination for Piecewise Continuous Constrained Optimization. CPAIOR 2018: 585-594 - [c4]Buser Say, Scott Sanner:
Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models. IJCAI 2018: 4815-4821 - [i2]Buser Say, Scott Sanner:
Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models. CoRR abs/1811.10433 (2018) - 2017
- [c3]Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner:
Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming. IJCAI 2017: 750-756 - [c2]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. NIPS 2017: 6273-6283 - [i1]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. CoRR abs/1704.07511 (2017) - 2016
- [c1]Buser Say, André A. Ciré, J. Christopher Beck:
Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility. ECAI 2016: 1044-1052
Coauthor Index
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last updated on 2024-10-15 21:36 CEST by the dblp team
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