| Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. A Hyttinen, F Eberhardt, M Järvisalo UAI, 340-349, 2014 | 129 | 2014 |
| Learning Linear Cyclic Causal Models with Latent Variables A Hyttinen, F Eberhardt, PO Hoyer Journal of Machine Learning Research 13, 3387-3439, 2012 | 121 | 2012 |
| Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure A Hyttinen, PO Hoyer, F Eberhardt, M Järvisalo Uncertainty in Artificial Intelligence, 2013 | 97 | 2013 |
| Experiment selection for causal discovery A Hyttinen, F Eberhardt, PO Hoyer Journal of Machine Learning Research 14, 3041-3071, 2013 | 92 | 2013 |
| Do-calculus when the True Graph Is Unknown. A Hyttinen, F Eberhardt, M Järvisalo UAI, 395-404, 2015 | 50 | 2015 |
| Causal Discovery from Subsampled Time Series Data by Constraint Optimization A Hyttinen, S Plis, M Järvisalo, F Eberhardt, D Danks International Conference on Probabilistic Graphical Models (PGM), 2016 | 37 | 2016 |
| Bayesian discovery of linear acyclic causal models PO Hoyer, A Hyttinen Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence …, 2009 | 34 | 2009 |
| Reduced cost fixing in MaxSAT F Bacchus, A Hyttinen, M Järvisalo, P Saikko Principles and Practice of Constraint Programming: 23rd International …, 2017 | 28 | 2017 |
| A logical approach to context-specific independence J Corander, A Hyttinen, J Kontinen, J Pensar, J Väänänen Annals of Pure and Applied Logic 170 (9), 975-992, 2019 | 26 | 2019 |
| Applications of MaxSAT in data analysis OJ Berg, AJ Hyttinen, MJ Järvisalo Proceedings of Pragmatics of SAT 2015 and 2018, 2019 | 24 | 2019 |
| Learning Optimal Chain Graphs with Answer Set Programming D Sonntag, M Järvisalo, JM Pena, A Hyttinen http://auai.org/uai2015/proceedings/papers/189.pdf, 2015 | 23 | 2015 |
| Identifying causal effects via context-specific independence relations S Tikka, A Hyttinen, J Karvanen Advances in Neural Information Processing Systems 32, NeurIPS 2019., 2020 | 22 | 2020 |
| Causal discovery for linear cyclic models with latent variables A Hyttinen, F Eberhardt, PO Hoyer Fifth European Workshop on Probabilistic Graphical Models (PGM-2010), 2010 | 22* | 2010 |
| Causal effect identification from multiple incomplete data sources: A general search-based approach S Tikka, A Hyttinen, J Karvanen Journal of Statistical Software 99 (5), 2021 | 19 | 2021 |
| Towards Scalable Bayesian Learning of Causal DAGs J Viinikka, A Hyttinen, J Pensar, M Koivisto Advances in Neural Information Processing Systems 33, NeurIPS 2020., 2020 | 17 | 2020 |
| A constraint optimization approach to causal discovery from subsampled time series data A Hyttinen, S Plis, M Järvisalo, F Eberhardt, D Danks International Journal of Approximate Reasoning 90, 208-225, 2017 | 17 | 2017 |
| Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables A Hyttinen, F Eberhardt, PO Hoyer Uncertainty in Artificial Intelligence, 2012 | 17 | 2012 |
| A core-guided approach to learning optimal causal graphs A Hyttinen, P Saikko, M Järvisalo Proceedings of the 26th International Joint Conference on Artificial …, 2017 | 16 | 2017 |
| Structure learning for Bayesian networks over labeled DAGs A Hyttinen, J Pensar, J Kontinen, J Corander International Conference on Probabilistic Graphical Models, 133-144, 2018 | 12 | 2018 |
| Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach K Rantanen, A Hyttinen, M Järvisalo International Journal of Approximate Reasoning 117, 29-49, 2020 | 11 | 2020 |
Patrik HoyerAlumni, Aalto University and University of HelsinkiVerified email at alumni.aalto.fi
