Please note that ISTA Research Explorer no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
459 Publications
2020 | Conference Paper | IST-REx-ID: 8324 | 
Wang, Peixin, Hongfei Fu, Krishnendu Chatterjee, Yuxin Deng, and Ming Xu. “Proving Expected Sensitivity of Probabilistic Programs with Randomized Variable-Dependent Termination Time.” In Proceedings of the ACM on Programming Languages, Vol. 4. ACM, 2020. https://doi.org/10.1145/3371093.
[Published Version]
View
| Files available
| DOI
| arXiv
2020 | Journal Article | IST-REx-ID: 15055 |
Brázdil, Tomáš, Krishnendu Chatterjee, Petr Novotný, and Jiří Vahala. “Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes.” Proceedings of the 34th AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, 2020. https://doi.org/10.1609/aaai.v34i06.6531.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 15082 |
Aichholzer, Oswin, Julia Obmann, Pavel Patak, Daniel Perz, and Josef Tkadlec. “Disjoint Tree-Compatible Plane Perfect Matchings.” In 36th European Workshop on Computational Geometry, 2020.
[Published Version]
View
| Download Published Version (ext.)
2020 | Conference Paper | IST-REx-ID: 7810 |
Chatterjee, Krishnendu, Amir Kafshdar Goharshady, Rasmus Ibsen-Jensen, and Andreas Pavlogiannis. “Optimal and Perfectly Parallel Algorithms for On-Demand Data-Flow Analysis.” In European Symposium on Programming, 12075:112–40. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-44914-8_5.
[Published Version]
View
| Files available
| DOI
| WoS
2020 | Conference Paper | IST-REx-ID: 8728 |
Asadi, Ali, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Kiarash Mohammadi, and Andreas Pavlogiannis. “Faster Algorithms for Quantitative Analysis of MCs and MDPs with Small Treewidth.” In Automated Technology for Verification and Analysis, 12302:253–70. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-59152-6_14.
[Submitted Version]
View
| Files available
| DOI
| WoS