MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning.
Neural Networks, 2022
Programming and Training Rate-Independent Chemical Reaction Networks.
CoRR, 2021
CRN++: Molecular programming language.
Nat. Comput., 2020
A study of the learnability of relational properties: model counting meets machine learning (MCML).
Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2020
Designing Neural Networks Using Logical Specs.
Proceedings of the 31st IEEE International Symposium on Software Reliability Engineering, 2020
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020
CRNs Exposed: A Method for the Systematic Exploration of Chemical Reaction Networks.
Proceedings of the 26th International Conference on DNA Computing and Molecular Programming, 2020
A Study of the Learnability of Relational Properties (Model Counting Meets Machine Learning).
CoRR, 2019
CRNs Exposed: Systematic Exploration of Chemical Reaction Networks.
CoRR, 2019
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees.
CoRR, 2019
Neural Program Repair by Jointly Learning to Localize and Repair.
Proceedings of the 7th International Conference on Learning Representations, 2019
: Molecular Programming Language.
Proceedings of the DNA Computing and Molecular Programming - 24th International Conference, 2018
File-level vs. module-level regression test selection for .NET.
Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 2017
Regression test selection across JVM boundaries.
Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 2017
Porting of run-time environment for Lua-based applications.
Proceedings of the IEEE 5th International Conference on Consumer Electronics - Berlin, 2015